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It is known that the device MOSFET works as a dosimeter in radiation beams for cancer radiotherapy. Basically, the radiation beam generates defects in a way that produces in the MOSFET the variation in its threshold voltage, VT, which is proportional to the accumulated radiation dose. Recently, MOSFETs were also tested under X-ray beams commonly used in the energy range applied to medical diagnoses, which is lower energy than radiotherapy. Indeed, it was already shown that the MOSFET drain current varies with the radiation dose for energies in the range applied to diagnostic radiology. In this paper, an innovative method based on a differential amplifier circuit with two electronically matched MOSFETs is presented. Therefore, before irradiation the differential output voltage signal, Vdif = 0. Actually, it is a differential amplifier circuit with one of the MOSFETs shielded from the ionizing radiation beam. Therefore, if such a circuit is irradiated the difference between ID1 and ID2, from MOSFET1 and MOSFET2 respectively, will result Vdif ≠ 0. To evaluate the response of this electronic dosimeter it was tested under a diagnostic radiology beam typically used in clinics to take radiography of patients. The results showed that the proposed dosimeter works and varies linearly with the radiation dose. The technology developed in this paper will allow research to be carried out for optimizing such electronic dosimeter to make it a wearable device and can be able to use it by workers exposed to ionizing radiation in nuclear power plants or even in outer space. [ABSTRACT FROM AUTHOR]

Cancer is a disease with a high mortality rate. Cancer is developed when the cell does not undergo a cell division process, the presence of uncontrolled cell growth turns to tumor cells and spread to various other organ systems. Early detection of such cancer improves the chances of favourable outcomes. Despite the fact that the early prognosis of cancer has made considerable improvement. Lung cancer is the second leading cancer worldwide. The identification of lung cancer and lung nodule segmentation is a crucial preliminary step. The Deep Learning network works best for medical image automation. Deep learning models and computer vision applications are expanding rapidly in the modern era. This paper includes Computed Tomography (CT) images to segment challenging lung nodules. The modified U Net introduced in this paper performs the segmentation of lung nodules. The dataset is obtained from the Radiology Department at Chennai Rajiv Gandhi General Government Hospital. The ResNodNet outproduces the segmented nodule, the model combines Respath and U Net to facilitate the categorization of multi-featured data that has been proposed as ResNodNet. The classification results produced by the suggested method are accurate at 98.6%. [ABSTRACT FROM AUTHOR]

The calcaneal fractures normally occur due to high-energy impact and 75% of these fractures are considered complex injuries. A patient-specific implant is not economically practical in production and has to deal with the patient anatomical contour bone surface. In addition, the ideal fitting of calcaneus plate for the Indonesian local patient and oversize plate implant are other issues. In this study, the bone morphological features of three-dimensional (3D) measurement are used to facilitate the customized plate design. The purpose of this paper is to find the geometric dimension of calcaneal plates and to develop a workflow for redesigning plate implants. The calcaneus bone was digitally reconstructed from computed tomography (CT) images of non-fractured injuries patients. A total of 37 anatomical landmarks were digitized on the polygonal surface modeling of the calcaneus. The computer-assisted measurement was employed to find the reliable length and width as well as other calcaneus morphometrical parameters. The computer-aided design tool platform was used for the design and simulation of plate implants. The result of this study provides a series of geometry data for design performing and fitting the plate implants to the surrounding surface of the fractured calcaneus. The customized implant design was reconstructed following the standard lateral plates to prevent the oversized implanted plate design. The paper describes a feasibility approach to measure specific calcaneus geometry and the information used in the design process for the reconstruction of calcaneus plate implants. [ABSTRACT FROM AUTHOR]

U-net is an image segmentation approach developed mostly for image segmentation tasks. The style includes a max pooling path to acquire context and a symmetrical up sampling track through concatenation that allows accurate localization. The success of U-net appears in its considerable use in mostly all considerable photo methods, from CT scans as well as likewise MRI to Xrays in addition to microscopy. In addition, while U-net is considerably a segmentation tool, there have actually been circumstances of utilizing U-net in various other applications. Considered that U-net's possibility is still elevating, in this paper, U-net model is trained on MADS dataset and afterwards the model's efficiency is reviewed with future suggestions and enhancements. [ABSTRACT FROM AUTHOR]

Recently, imagery of the chest has become the key clinical procedure for diagnosing and predicting chest infection in the lungs. Computed tomography (CT) images of the chest were thus considered in this study as a screening strategy for early-stage detection of chest infections and other abnormalities in the human lung. Raw computed tomography is difficult to interpret, leading to a need to develop computer algorithm diagnostic (CAD) approaches to improve the detection of abnormalities in the resulting CT images. The data samples used in this paper were obtained from Al-Hussein Teaching Hospital and the Radiology Department of Imam Al Hujja Charity Hospital in Iraq, Kerbala. The number of images in the assembled dataset was 150 across two different class types, normal and with confirmed lung disease. The Fuzzy C-Means Clustering (FCMC), K-Means Clustering (KMC), and Gaussian Mixed Model Clustering (GMMC) techniques were then applied to the chest CT images to test the detection and classification of the normal and infected scans. The features of the CT images in this paper were then filtered to remove clusters identified as belonging to normal areas to develop a full algorithm for the identification of abnormal areas by mans of anatomic segmentation of chest infection Region of Interest (ROI), which is presented as part of this work. The experimental results show that the proposed segmentation techniques offered clustering accuracy of 93.12 %, 90.23%, and 91.41% for FCMC, KMC, and GMMC, respectively. The performance metrics thus show that the FCMC algorithm outperforms the KMC and GMMC algorithms, as well as being fully autonomous, and having the capability to isolate abnormal infected regions in the lung tissue where such anomalies exist accurately to the benefit of radiologists, a function few other computational algorithms can offer. [ABSTRACT FROM AUTHOR]

This paper presents the detailed overview on the artifacts related to Computed Tomography (CT) specifically Cardiac CT (CCT), their classification and various reduction techniques. In CCT, the motion artifacts occurs mainly due to the pumping of the heart which has to be reduced during and post CT reconstruction. The main drawback of CT machine is that it emits high radiation during scanning. Therefore, Low Dose CT (LDCT) and Limited Angle CT (LACT) have been proposed by various researchers to overcome the problem of emission of high radiation during scanning which provides better image quality with minimum radiation. However, the artifacts generated during scanning and in image reconstruction phase also affects the quality of the image which makes it a challenging task to medical experts during their interpretation. In view of that, the extensive research have been carried out by various researchers to reduce the noises or artifacts using traditional and machine learning techniques in recent past. Finally, the paper is concluded by presenting the current state of art deep learning approaches to enhance the performance of the existing methods. [ABSTRACT FROM AUTHOR]

This paper presents the comparison of filtering methods for a contrast enhancement of computed tomography (CT) brain images. Each method consists of three filter consecutively which is a combination of the low order linear filter such as Gaussian filter, disk filter, average filter and median filter with an adaptive filter method and unsharp filter. The process starts with filtering the CT brain image using low order linear filter, then proceeds with adaptive averaging filter and ends with unsharp filter. In this paper, there are two criteria, peak signal to noise ratio and mean square error, that were adopted for performance assessment. Our preliminary results showed that the combination of Gaussian filter with adaptive filter and unsharp filter gives the good result in removing the noise and edge detection. This method improved the CT brain image and the gyri and sulci can be easily identified. [ABSTRACT FROM AUTHOR]

Deep learning techniques have recently gained increasing attention not only among computer science researchers but are also being applied in a wide range of fields. However, deep learning models demand huge amounts of data. Furthermore, fully supervised learning requires labeled data to solve classification, recognition, and segmentation problems. Data labeling and annotation in the medical domain are time-consuming and labor-intensive. Semi-supervised learning has demonstrated the ability to improve deep learning performance when labeled data is scarce. However, it is still an open and challenging question on how to leverage not only labeled data but also the huge amount of unlabeled data. In this paper, the problem of pancreatic cancer detection on CT scans is addressed by a semi-supervised learning approach based on pseudolabeling. Preliminary results are promising and show the potential of semi-supervised deep learning to detect pancreatic cancer at an early stage with a limited amount of labeled data. [ABSTRACT FROM AUTHOR]

The pandemic COVID19 that has been emerged around the world and Induced by a member from a family of corona viruses named (SARS COV-2) that has appeared in Wuhan in 2019 and can lead to sever acute respiratory syndrome with grave complication and even death of the infected person. The detection of persons infected with the virus is most important, as the virus as it can be easily transmitted from one to another and the person infected with the virus will also not know that he is infected until he has a number of symptoms. The detection of the virus is performed in this paper using deep learning as part of monitoring this outbreak, researchers began using software computing techniques to diagnose cases using chest CT-Scan images and X-Ray for the lungs, scan body temperatures and classify the severity of the disease. The research objective is to detect three classes: Covid-19 positive, Normal, and eumonia based on both X- Ray and CT-images. The importance of this research is to support the medical staff in Mosul city, in particular in the case of a heavy workload. The detection technique begins with some pre-processing tools for image processing to strengthen contrast, and then deep learning as the Convolution Neural Network (CNN) is used for detection. CNN will be based on the public dataset of COVID19 for training and forecasting other cases for the future. The programming language used in this paper is Matlab and the results of this study indicate that the best accuracy is obtained from the model with 99.55 %, 99.09 % sensitivity and 99.48 % precision of chest CT, but when the X-ray dataset is used The proposed model has achieved a classification accuracy of 85.58 %, 83.47% sensitivity and 87.33% precision. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is a life-threatening medical condition caused by high blood sugar, high blood pressure, alcohol, smoking, and congenital abnormalities. There are several ways to diagnose CVD, such as stethoscopes, electrocardiography (ECG), phonocardiography (PCG), and computed tomography (CT). Some of these devices are inaccurate and require medical expertise. In stethoscopes, the doctor's skill determines heart sound interpretation and external disturbances can affect their effectiveness. Additionally, ECG and PCG cannot provide complete heart health information. CT scans, however, emit radiation that can harm patients. This study examines various CVD early detection and prediction methods to find the best one for patients and doctors. Previous research has identified methods, including artificial intelligence (AI) algorithms and wireless sensors. The study also seeks to identify the best model and examine CVD detection challenges. Comparing previous research, the modified scalp swarm optimization method and adaptive fuzzy neurological system (MSSO-ANFIS) had the highest rating accuracy, 97.45%. Researchers' CVD detection difficulties were also highlighted in the paper. In conclusion, the use of echocardiography devices provides comprehensive and accurate information about the heart, including details such as the shape of the heart, the thickness of the heart muscle, and heart sounds. [ABSTRACT FROM AUTHOR]

Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Deep learning tools deal with many types of medical images, including computed tomography (CT), X-rays, and magnetic resonance imaging (MRI). MRI is the most common method for diagnosing diseases of the lumbar spine. This paper aims to provide a general overview of how deep learning can be used to analyze lumbar spine images. It focuses on papers, results, and methods used by researchers in recent years. The presented works indicate that deep learning can be highly relied upon in the process of analyzing medical images of the lumbar spine and identifying the correct diagnosis. [ABSTRACT FROM AUTHOR]

Truthful examination and detection of lung nodules are very crucial for prevention of lung cancer deaths. Radiologists have to spend their huge time for diagnosis of lung nodules from computed tomography (CT) images. Moreover, there is a high possibility of false positive or false negative results even after detecting lung nodule candidates. So, an effective computer aided detection (CADe) tool that can detect and classify lung nodules in lung CT images may be of immense help in this aspect and so researchers throughout the globe have been working on it. We too have been engaged in this field with an objective of developing efficient and novel CADe systems. This paper describes our developed 2D convolution neural network (2D CNN) model for classification of lung CT images into 2 important classes viz. benign & malignant. In our study, we have used 206 lung CT images in DICOM format collected from LIDC-IDRI open source database. Total 9 layers viz. input layer, convolution layer, ReLU Layer, convolution layer, ReLU layer, fully connected layer, fully connected layer, softmax layer and classification layer respectively have been used in the model. Experimental result of our model has achieved satisfying result with 63.64 % accuracy at initial learning rate: 0.01; validation frequency: 50; max epoch: 30 and mini batch size: 128. [ABSTRACT FROM AUTHOR]

Pleuropulmonary blastoma (PPB) is a very aggressive and rare type of cancer that occurs mainly in children; however, it may also affect adults. PPB originates from the lung or the pleural cavity, but its cause is still unknown. PPB has four different subtypes based on whether it involves cysts and/or cancerous nodules, as well as considering the age at diagnosis and prognosis. Medical imaging plays an important role in the diagnosis of PPB to quantify the extent of metastasis of the tumour, such as in type II and type III, and to titrate the treatment strategy accordingly. In this paper, the case of a deceased 3-year-old boy, who was diagnosed with type II and III PPB, is presented, including an analysis on related medical images involving both ionising (chest X-ray and computed tomography) and non-ionising techniques (ultrasound), as well as the treatments that the child underwent. The patient received surgical resection of the tumour, followed by chemotherapy; however, local tumour relapse occurred, a second resection was not possible, and the relapsed tumour did not respond to chemotherapy leading to the death of the child. [ABSTRACT FROM AUTHOR]

This paper presents a new workflow for the improved visualization of liver lesions that are very small in size. This is because, these lesions are neither properly visible on CT scans nor on US images. Thus, the clinicians while performing the procedure face a lot of challenges due to weak mental registration, which is not optimal. The challenges lead to undesirable consequences that are neither useful for the patient nor for the clinician. [ABSTRACT FROM AUTHOR]

This paper presents the results of studies to assess the effect of wollastonite on the properties of fine-grained concrete mixes. The research of mechanical characteristics confirmed the increase in the initial strength of concrete. This fact contributes to the fulfillment of the conditions for the applicability of concrete as a printed material in additive technologies. Tests at later curing times also confirmed the effectiveness of adding wollastonite in an amount of 6 percent by weight of the cement. An analysis of the rheological characteristics showed a reduction in the setting time of solutions with wollastonite compared to the control composition. The formation of the material structure was investigated by using X-ray computed tomography. [ABSTRACT FROM AUTHOR]

Medical images are images of the interior body taken using medical imaging techniques. Most of these images are contaminated with noise caused by transmission limitations or storage devices during the process of image collection from MRI, CT scan, or biopsy procedures. To date, many researchers have found that image denoising is a difficult task because they must strike a balance between noise reduction and preserving sharp image edges. Instead of conventional denoising methods, this paper studies the application of Shearlet transform for denoising the medical image. Shearlet transform is able to provide better directional determination during image reconstruction because it has the properties of a multiscale and is able to extract anisotropic features on images. In this study, different thresholding methods such as hard threshold, Otsu threshold and adaptive threshold were considered for denoising the image. In this study, peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similar index (SSIM) are used for performance evaluation. The experimental results show that the Shearlet transform is suitable to be used for denoising the medical images, where the most suitable thresholding method is the hard threshold, which it effectively reconstructs the images and preserve the edges on medical images compared to the other considered thresholding methods. [ABSTRACT FROM AUTHOR]

A non-invasive method to predict the occurrence of stenosis isintroduced and used in this paper to analyze the diameter profile of coronary artery blood flow. In the arterial profile, differences in sex and age can affect the size of the coronary artery diameter. The coronary artery segmentation process uses threshold segmentation with a value of 130 HU on Computed Tomography Angiography (CTA) images. Arterial diameter profile was obtained from centerline extraction on image segmentation results with 64 male patients and 30 female patients divided into age ranges; 35 – 44 years, 45 – 54 years, 55 – 64 years, and 65 – 74 years. The average diameter measurement was carried out on the Right Coronary Artery (RCA) and Left Anterior Descending (LAD) vessels, each of 120 mm long, divided into 6 length categories. Based on gender, men have a larger coronary artery diameter than women for each category with a significance test on the RCA (p<0.05) and LAD (p>0.05). At a length of 120 mm, LAD provides a more drastic reduction in diameter than RCA with the difference in the average diameter of male LAD (0.35±0.12)/20 mm; female (0.33±0.16)/20 mm and male RCA (0.25±0.13)/20 mm ; female (0.27±0.11)/20 mm. The outliers show the difference in the drastic profile reduction due to the narrowing and branching of the coronary arteries which are evaluated based on the 3D image of the arteries. [ABSTRACT FROM AUTHOR]

Image pre-processing, also known as image enhancement, is a necessary first stage in basically all image processing applications. The quality of the sequent phases and the full application is highly dependent on outcome of the pre-processing stage. The removal of noise and soft tissue from CT images is an essential pre-processing step. During the filtering of both noise and soft tissue in images, the greater issue is maintaining location accuracy and image details without losing them. This paper describes an improved CT image pre-processing approach based on histogram modeling that reduces noise and undesirable artifacts and shows the external structure of the bone by taking image contents into account. Furthermore, the bone tissue is enhanced while the image texture's location accuracy is maintained. The powerful MATLAB software was utilized to test performance of the enhanced algorithm. In addition, the mean square error, peak signal to noise ratio, edge preservation index, and entropy are used to statistically compare the results and therefore evaluate their performance. When compared to the previous technique, the enhanced method produces better results. [ABSTRACT FROM AUTHOR]

Significant progress has been made in the field of lung cancer over the years (chemical rehabilitation, radiotherapy, individualized treatment). Nevertheless, lung cancer remains a threat to society and a leading cause of death for thousands of people worldwide. The subject of this paper is the diagnosis of lung cancer. Lung cancer is diagnosed using computerized tomography (CT) scans. To accurately diagnose lung cancer, different algorithms are used. The image is filtered through a sharp hidden filter in this case. The edges and areas of the cancer are detected using a flexible canvas method. To identify the features andpredict the risk of lung cancer, using the neural network. The K-Nearest Neighbors method is used to separate cancerand lungs. Lung cancer is diagnosed using computerized tomography (CT) scans. To accurately diagnose lung cancer, different algorithms are used. The image is filtered through a sharp hidden filter in this case. The edges and areas of the cancer are detected using a flexible canvas method. To identify the features and predict the risk of the Using a neural network to study lung cancer. The K-Nearest Neighbors method is used to separate cancer and lungs. Lung cancer has been growing in recent years, so it is very important to know if tumor has turned into cancer. Prognosis occurs early and Accurate predictions can save many lives and assist doctors in starting therapy. CT (computed tomography) is a type of imaging that uses a is important in determining the condition of a plant by determining its size, location, and other factors. [ABSTRACT FROM AUTHOR]

Malignant Tumor in the Lung appears to be a number one explanation for death for people everywhere the globe. Early identification of carcinoma can improve people's chances of survival. When carcinoma is identified early, the 5-year survival rate jumps from 14 to 49 percent. Although CT scans are more efficient than X-rays. However, thanks to a time constraint in detecting the presence of carcinoma using the numerous diagnosing methods used, the matter seemed to merge. As a result, a picture processing-based carcinoma detection method is utilised to classify the presence of carcinoma in CT scans. Energetic Curvature segmentation is employed during this paper. RBFN classifies the stage of the Tumor. MATLAB was employed in this study for all of the processes. Image processing techniques have recently been popular during a sort of medical fields for image enhancement during the first phases of detection and treatment. The time factor is critical in detecting abnormality concerns within the target. The analysis and enhancement of image quality are both passionate about Low-pre-processing approaches which are utilised at the advance stage. [ABSTRACT FROM AUTHOR]

This paper presents an approach to study the structure of modified multicomponent fine-grained concretes based on the results of computer tomography. The proposed technique for processing layer-by-layer halftone images makes it possible to evaluate the course of hydration and structure formation of the composite at all stages of hardening. The development of non-destructive testing methods capable of correctly identifying the structural units of the system at the early stages of hardening in order to analyze the overall picture of hydration contributes to the operational control of cement concretes characteristics at a qualitatively new level. [ABSTRACT FROM AUTHOR]

The increase of current transformer (CT) failure rate in PLN P3B Jawa and Bali within the last five years has put CT as a critical asset. In order to prevent existing CTs from the similar failure, the assessment method needs to be evaluated. For this purpose, further investigation was conducted on three sister units of two failed CTs by performing measurements and tear down. The investigations reveal that the insulating medium of the high voltage conductor-to-ground of two investigated CTs were deteriorated due to moisture ingress. The presence of carbonized spots and residual material was also found in particular on the insulating paper. The physical conditions of each investigated CT are analyzed to determine the ageing stage which represents the failure risk. The correlation between the failure risk and its testing results is analyzed to evaluate the assessment method of CT operated under tropical climate. [ABSTRACT FROM PUBLISHER]

In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels. [ABSTRACT FROM AUTHOR]

Image based concrete finite element model is now becoming popular to determine the mechanical properties of concrete. In this review paper researchers had prepared a concrete cube. Their compressive strength is determined in laboratory. Images of the cubes were taken by medical CT scan device. 2D images were converted into 3D images. Finite element model was prepared by using various software. Stress – strain relationship can be found out by finite element model. [ABSTRACT FROM AUTHOR]

The Electroencephalogram (EEG) signal is popularly used technique to analyse the brain activities and measured using Scalp electrodes which is quite useful in detecting status of the brain and epilepsy of a subject and also supplement the CT scan measurement. EEG signals are indirectly indicate the condition of Brain. EEG signals are sampled at 256 samples/second for a sixteen epochs are considered for this work. In this paper the features of normal and seizure EEG signals are extracted using statistical technique and classified using Detrend Fluctuation Analysis, Detrend Fluctuation Analysis EM, Detrend Fluctuation Analysis log regression, Detrend Fluctuation Analysis Non Linear regression, Detrend Fluctuation Analysis Linear regression. The classifier performance is compared using the standard parameters like sensitivity, specificity, Accuracy, Performance Index and False Alarm. The highest accuracy of 98.96% obtained for the hybrid classifier Detrend Fluctuation Analysis Linear regression for Normal EEG signal and an accuracy of 98.96 % achieved using Detrend Fluctuation Analysis with Expectation Maximization classifier for seizure EEG signal. [ABSTRACT FROM AUTHOR]

The main is one of the main directions and goal of image reconstruction technology a predetermined quality, as in improving the quality of an image and restoring its blurred or degraded parts. Overlapping and blocking some part of the image, image quality improvement basically, image acquisition is a subjective phenomenon, and image restoration is an objective phenomenon. Restoration, i.e. restoration and restoration of the degraded image using existing knowledge about the degradation phenomenon, is important. Therefore, restoration methods focus on modeling the distortion and applying an inverse process to restore the original image. This study examines linear, space-invariant recovery models applied to various recovery situations. It also covers the basic methods of image reconstruction from shadows or projections and their use in medical computed tomography, which is an effective image processing tool, especially commercial and useful in biotechnology and medicine. This research paper presents the analysis of Gaussian, Rayleigh, Erlang, Exponential, Uniform, Salt-and-Pepper, Periodic, Estimating noise and their main parameters. [ABSTRACT FROM AUTHOR]

Computed tomography is one of the most widely used educational methods in medicine. This method allows you to create images of the internal structures of the body, eliminating the need for invasive procedures or surgery. Due to the increasing complexity and diversity of diseases in modern medicine, there is a need for more accurate and informative educational methods that would allow more effective diagnosis and treatment of various diseases. This paper examines the integral Radon transform in which integration is not performed along a straight line, but along a circle of radius. During the study, a formula for inverting the Fourier image in the first variables was obtained. Using regularization of ill-posed problems based on Tikhonov's work, a sequence of approximate solutions is developed that approaches the exact solution of a given problem. [ABSTRACT FROM AUTHOR]

Covid-19 is a deadly disease that has spread all over the world. It has a larger social influence. There are various ways for diagnosing the virus, including RTPCR, Xray, and others, but chest computed tomography(CT) is one of the most effective. CT images are employed to identify if there any abnormalities and level of abnormalities identification of abnormalities in CT images is highly challenging. This paper focuses on the classification of CT images into normal or abnormal and also to localize the lesions in the CT images. Here lung lesion localization for accurate screening of covid 19 in CT images is proposed by incorporating transfer learning and K-mean clustering. The proposed approach is based on Resnet 50 architecture followed by dense network for classification [ABSTRACT FROM AUTHOR]

Tampered Medical images and scans are a very serious issue in the medical field. The results of tampered scans can range from mild to serious results. For example, a tampered scan or medical image can lead to misdiagnosing a patient with a serious condition or a patient who suffers from a serious condition can be misdiagnosed as healthy leading to a delay in receiving proper treatment. In this paper, we propose a deep learning-based methodology to detect tampered/fake cancers in 3D CT scans of human lungs. We use a publicly available dataset of deepfakes that include both tampered and genuine cancers and propose the use of a modified Convolutional Neural Network (CNN), in particular the AlexNet with Transfer Learning. The model was pre-trained on a large dataset and fine-tuned on the medical images dataset to detect tampering. Experimental results achieve a high level of accuracy in detecting tampering. Using the AlexNet with Transfer learning, we achieved an accuracy of 89.47%, Recall of 89.47%, Precision of 89.47%, and F1 measure of 89.47%. This is higher accuracy than similar methods using the same dataset reported in the extant literature. Achieving this high accuracy for a multiclass complex problem is considered an excellent achievement. Expert radiologists are mostly unable to distinguish between real and tampered cancer images. The results demonstrate the potential for deep learning-based models in improving accuracy and efficiency in tampered medical scan detection. [ABSTRACT FROM AUTHOR]

Early identification and diagnosis of COVID-19 are critical factors in relation to infection constraints. Computed tomography imaging is considered a golden modality for detecting and evaluating lung COVID-19 infection. In this paper, a currently proposed algorithm has been developed for the three-dimensional visualization of infection regions, where the segmentation algorithm combines a grayscale morphological reconstruction technique with a fast marching method, then reconstructing the stacked two-dimensional infection regions into a three-dimensional faction based on a strategy of marching cubes, the results showed the consistent determination of ground glass opacity infection region compared to the manual delineation of a senior physician, The suggested approach promotes robust medical image analysis based on sparse data and improves COVID-19 segmentation technology state of the art. [ABSTRACT FROM AUTHOR]

Pulmonary embolism (PE) is an important clinical disorder that will result in lung tissue damage or low blood oxygen levels, which need early diagnosis and timely treatment. While computed tomographic pulmonary angiography (CTPA) is the gold standard to diagnose PE, previous studies have verified the effectiveness of combing CTPA and EMR data in computer-aided PE detection or diagnosis. In this paper, we proposed a multimodality fusion method based on multi-view subspace clustering guided feature selection (MSCUFS). The extracted high-dimensional image and EMR features are firstly selected and fused by the MSCUFS, and then are feed into different machine learning models with different fusion strategy to construct the PE classifier. The experiment results showed that the joint fusion strategy with MSCUFS achieved best AUROC of 0.947, surpassing other early fusion and late fusion models. The comparison between single modality and multimodality also illustrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%. [ABSTRACT FROM AUTHOR]

PET and CT image registration is an important tool of clinical diagnosis of diseases. For PET and CT images, a preprocessing algorithm of medical image registration is proposed in this paper. The algorithm process includes image normalization, CT image adaptive threshold adjustment and automatic extraction of tissues based on morphology, edge detection and statistical analysis theory, and improved PET image interpolation based on physical spacing of pixels. The experiment results indicate that the preprocessing methods proposed in this paper have a great effect on PET and CT image registration research because of the exclusion of redundant information and the improvement of the registration algorithm efficiency. [ABSTRACT FROM PUBLISHER]

Objective: Waiting time for a consultation for chronic pain is a widespread health problem. This paper presents the design of an ontology use to assess patients referred to a consultation for chronic pain. Methods: We designed OntoDol, an ontology of pain domain for patient triage based on priority degrees. Terms were extracted from clinical practice guidelines and mapped to SNOMED-CT concepts through the Python module Owlready2. Selected SNOMED-CT concepts, relationships, and the TIME ontology, were implemented in the ontology using Protégé. Decision rules were implemented with SWRL. We evaluated OntoDol on 5 virtual cases. Results: OntoDol contains 762 classes, 92 object properties and 18 SWRL rules to assign patients to 4 categories of priority. OntoDol was able to assert every case and classify them in the right category of priority. Conclusion: Further works will extend OntoDol to other diseases and assess OntoDol with real world data from the hospital. [ABSTRACT FROM AUTHOR]

The chronic lung disorder grouped under the Interstitial Lung Disease (ILD), like asthma, pneumonia, infection in the respiratory tract, helps make decisions based on human studies with non-imaging techniques. These disorders are mainly due to particle deposition in the lung. Pattern recognition algorithms do accurate identification of disorders. In this paper, the classification and prediction of different lung diseases are done with a Computed Tomography (CT) scan image. The proposed model utilizes the Support Vector Machine (SVM) network model classifier to classify the normal and abnormal input CT scan image and achieve higher accuracy. The binning method splits an image into several sub-images, specifically reaching near the damaged region and carrying out assessment just on the chosen sub-image. Some features like reflection coefficients, mass density, and tissue impedance are evaluated for 3D image projection of Region of Interest (ROI). The performance is evaluated using the confusion matrix with the predicted values for the test data. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to be lowered to a minimum of delay and not to provide an early forecast of cancer of lung cancer in patients, but also and not to reduce the need for biopsy. In addition, it gives and comparisons between different algorithms for classification. Existing models use a CT scan to predict lung cancer but are also well equipped with sufficient data to predict precisely. The model was developed based on a support vector machine, which provides 92 % accuracy. Because it is required solutions, for to be increased right speed for prognosis of cancer of lung cancer. The proposed solution uses a set of data for classification and has approximately 300 observations. The model is developed for different algorithms for classification, such as arbitrary attendant's vectors, K-nearest-neighbors, and Naive-Bayes are compared to a - great accuracy. It provides an early prognosis of cancer of lung cancer with high accuracy. Therefore, the proposed decision model based on Naïve Bayes achieved the highest degree of accuracy of 95.23%. [ABSTRACT FROM AUTHOR]

The cancer death in concern to lung cancer is increased compared to other cancers worldwide. The presence of nodules in the lung indicates the chances of getting lung cancer in the future. In this paper, an accurate Computer-Aided Detection (CAD) system for lung nodules detection using Computer Tomography (CT) images is proposed. It consists of four important modules such as preprocessing; Two-Successive Segmentation Process (TS2P), Rule-Based Refinement Pass (RBRP), and Detection Module (D.M.DM). The lung C.T. CT image is de-noised using the Wiener filter in the preprocessing module. In the TS2P module, the right and left lungs are segmented at first, and in the next stage, the nodules and vessels are segmented. Then, the RBRP module is designed to remove the vessels with the help of geometrical features. Finally, the nodules are detected using a deep learning approach in the D.MDM. The proposed method is validated on 888 lung C.T.CT images, and a mean average precision of 96.75% and sensitivity of 97% with 2 false positives per image were obtained. [ABSTRACT FROM AUTHOR]

The current paper focuses on correlating the region of ischemic stroke based on CT-scan and EEG image visualization, approaching using Brainstorm and BSI on each pair. Ischemic stroke occurs when the flow of blood containing oxygen and supplements to the brain is blocked, causing the body to break down. In arrange to identify stroke, we generally use CT and MRI, which have succeeded in determining the location with high accuracy. The factor is the high price of CT-scan or MRI, so we want to find another alternative with a low-cost and practical approach to using EEG. The most recent research on EEG in detecting stroke uses a signal processing approach based on time series and frequency to obtain abnormalities from the signal graph with the initial suspicion of blockage of blood vessels within the brain. Research is related to finding indications of stroke by using the brain symmetry index, which is to find the symmetry value of the relationship between the two-sided electrode locations. This study uses a signal processing approach to become an image with the help of the Brainstorm toolbox software released by the University of Southern California. The data used came from patients who experienced stroke symptoms with fewer than 72 hours. The current inquires about the investigation is based on recognizing the electrical current density in a data grouping based on the region of interest on the cortical surface in ischemic stroke conditions. All the patients were examined for ischemic stroke based on CT, which experts analyzed from the National Brain Center Hospital. Data processing is done by calculating the Brain Symmetry Index (BMI) and visualizing the data with Brainstorm on each EEG data. These results explain a correlation between expert CT-scan diagnoses to BMI values and are supported by data visualization using Brainstorm. [ABSTRACT FROM AUTHOR]

The collaboration of surface science and molecular biology has given birth to a new science called nano-biology. This paper highlights the latest improvements in nano-particles for biomedical imaging. They can not only provide better imaging performance, but they can also offer greater versatility of multimodal and targeted imaging. Nanoparticle-based probes are still far from practical use in clinics, due to safety concerns and less-optimized efficiency. This paper review highlights the recent advances in the development of inorganic Nanoparticle-based contrast agents for magnetic resonance imaging and computed tomography. Finally, a conversation on the future scope of nano-particle agents is given in this paper. We present the effect of contrast agents in Magnetic Resonance Imaging (MRI) of the brain by showing the MRI images of a patient’s brain T1 and T2 relaxation processes. The contrast agents show better resolution of the brain. The MRI image without the use of contrast agents clearly appears to be inferior to that one with the contrast agent. [ABSTRACT FROM AUTHOR]

Computed Tomography (CT) is far and wide utilized to make a diagnosis and access thoracic diseases. The enhanced resolution of CT examination has resulted in a considerable investigation of statistics for analysis. Computerizing the scrutiny of such facts is consequently necessitate and fashioned a hastily emergent research region in medical imaging. The finding of thoracic diseases by means of image processing directs to a pre-processing step identified as "Lung segmentation" which portrays a wide range of techniques starts with simple Thresholding and numerous image processing elements are incorporated to progress segmentation, precision and heftiness. In image processing, techniques like image pre-processing, segmentation and feature extraction have been thrashed out in detail. This paper suggestions investigation of literature on computer examination of the lungs in CT scans and statements the Preprocessing ideas, segmentation of a choice of pulmonary arrangements, and Feature Extraction intended at recognition and categorization of chest abnormalities. As well as, research developments and disputes are recognized and instructions for further examinations are discussed. [ABSTRACT FROM AUTHOR]

Image processing methods are used to support decision-making processes of radiologists for improving treatment for lung disease. These methods involve using chest CT images to diagnose and detect abnormal cases in lung. Improving the human visual perception of pulmonary CT images is favorable for the doctor to make a more accurate clinical diagnosis. Thus, improving pulmonary CT as a preprocessing procedure can increase accuracy and efficiency in actual medical applications. In this paper we propose an adaptive algorithm for improving the visual quality of CT images. It consists of contrast enhancement based of combination of Retinex theory and Contrast limited adaptive histogram equalization (CLAHE). A complex adaptive filter based on Wiener and median filtration is available to reduce existing noise. In order to simplify processing we use a tensor presentation of the images. The evaluation of the efficiency of the proposed algorithm is made by objective parameters such as PSNR, SNR, effectiveness of filtration and entropy in the original and processed images. The experimental results show very good results in the diagnosis of Covid 19, as well as in other inflammations of the lungs, compared to other similar methods known in the literature. [ABSTRACT FROM AUTHOR]

The standard test method for the compressive strength of the cylindrical specimens represents one of the ways to characterize building materials. The test results depend on the ratio of the specimen's length to cylinder diameter (slenderness ratio). This relation is well known and researched for typical building materials (concrete, mortars) and some rocks. The respective standards recommend ways to solve this problem (e.g., using the slenderness correction factors). The situation is different for newly developed and non-traditional materials. Therefore, this paper deals with the effect of the slenderness ratio on the compressive strength value of selected fine-grained alkali-activated aluminosilicate composites: i) with a ceramic precursor, and ii) with fly ash. In total, 25 specimens with a diameter of 50 mm were tested for each investigated material. For the standard test method for compressive strength determination, six sets of specimens were prepared. Each set of specimens differed in the slenderness ratio, which was set to 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0. Based on the obtained compressive strength vs. the slenderness ratio relationship, the selected specimens were analyzed and characterized with the use of 3D high-resolution X-ray computed tomography. [ABSTRACT FROM AUTHOR]

The medical Image fusion is the process of combining relevant information from a set of images into a single image in which the fused image contains more information than any of the input images. This paper presents a comparative study of two different medical image fusion algorithms such as PYRAMID method and DWT method along with its performance analysis. Here a novel Multi-Resolution Image Fusion using CT and MRI was done. To perform image fusion, PYRAMIDAL method and DWT method were applied to 9 sample pairs with (256x256) of Magnetic Resonance Images (MRI) and Computed Tomography (CT) images taken from NLST and MIDAS Database respectively. Result: Determination of the SNR and Correlation of fused image was successfully done. The SNR and Correlation of fused image with source images in pyramid method was 11.84 and 1 and using DWT method it was found to be 22.37 and 0.945 respectively. The results obtained were considered to be error-free since it was having the significant power of 0.002 with pre-test power of 95% in SPSS Statistical analysis. For the given samples, the DWT Method was found to be efficient compared to the PYRAMID method in performing medical image fusion. [ABSTRACT FROM AUTHOR]

The aim of this paper is to compress the computed tomography (CT) scan images using wavelet and shearlet transform and compare the compression ratios to find a better transform among them for the target application. The sample size of each group was 30 and the total sample size was 60. Compression ratios (CR) were generated for images based on the transform used for size reduction. Wavelet based CR appeared to be of higher mean (6.6146) when compared to shearlet (4.02). Hence wavelet transform seems to be a better transform for compressing CT scan images. We observed that wavelet transform seems to have a better compression ratio compared to shearlet transform (P<0.001, independent sample t-test). [ABSTRACT FROM AUTHOR]

In this paper, a new registration method that based on shape information and gray information has been introduced to the procedure of CT-MRI registration. In the first, the frames of the two images to be registered were explained by the principle of mechanics decomposition, then we coarsely registered the two images by their frames and as a result the error of the registration was limited in a small region. In the second step, we re-registered the two images that have been coarsely registered by means of maximum mutual information (MMI), and finally an accurate registration result was obtained. The simulation result shows this method is less time consuming and on the other side the registration accuracy is also guaranteed. Finally we successfully applied the present method to register the CT images and MRI images in a patient undergoing neurosurgery. [ABSTRACT FROM PUBLISHER]

Laboratory experiments investigated that ultrasonic wave stimulation can effectively enhance permeability of coal and improve production of unconventional reservoirs in field. The basic mechanism of coal-cleat effects on ultrasonic wave stimulation induced mechanical vibration in coal is poorly understood. In this paper, CT scan slices of coal sample are applied to conduct real numerical coal model to investigate effects of different physical attributes of materials in coal on ultrasonic wave propagation. Numerical simulation results reveal that cleats filled with minerals of higher velocity and density contribute to losing mechanicalvibration, but cleats filled with water or gas can trap the mechanical vibration at the interfaces of cleats. With the accumulation of mechanical vibration at interfaces of cleats, the permeability of coal will be enhanced. [ABSTRACT FROM AUTHOR]

The Gurson-Tvergaard-Needleman (GTN) model, is widely used to predict the failure of materials based on lab specimens, The direct identification of the GTN parameters is not easy and its time and money consuming. The Gurson model is based on micro-mechanical behavior of ductile fracture, containing void nucleation, growth and coalescence The most used method to determine the GTN parameters is the combination between the experimental and FEM results In this paper we are going to determine the GTN parameters for the SENT specimen based on the fracture toughness test of CT specimen. The reason behind choosing the SENT specimen is because it can be very good representative of the pipe for both uniaxial and biaxial loading conditions. [1] The Results show that the GTN parameters concluded from CT simulations, predict very well the crack initiation and propagation of SENT specimen which confirm the validity of this model, as already proved in the literature. [ABSTRACT FROM AUTHOR]

The paper presents research results from combusting high-energy solid rocket fuels (propellants) in laboratory rocket motor (LRM). The process of combusting high-energy fuel is characterized by fast chemical reaction, which causes creating a gaseous product with high pressure, which is able to produce a secondary system of fractures in the coal. The value of pressure from combusting propellants can be controlled in a laboratory rocket motor by an appropriate selection of mass of the fuel and the diameter of the nozzle. For testing purposes, the nozzle was replaced with a coal cylinder samples, which were directly affected by the pressure of powder gases in the sealed combustion chamber in the test system. With the use of sensors, pressure inside the combustion chamber was registered during every attempt of propellants’ ignition. Additionally, to assess the effectiveness of fracturing, coal samples were scanned by X-ray computed tomography (CT) before and after the fireground tests. The obtained roentgenograms were processed using specialized computer software for 3D images and data of test results. The aim of the paper is to present at and compare the data of fractures growth and pressure changes, which were created in result of combusting high-energy materials and coal fracturing. [ABSTRACT FROM AUTHOR]

Humans have a pair of lungs which is one of most vital and sensitive organs of the human body. Computed tomography (CT) is a diagnostic medical test that uses x-ray equipment to detect various health conditions. Also, this method is regarded as painless, noninvasive and accurate. Thus, we have obtained CT scanned images of three healthy subjects and in this paper we aim at obtaining signal to noise ratio after image extraction and segmentation. Then, filtering (using five filters) followed by enhancement using Adaptive Histogram Equalization is carried out. After enhancement, we calculate the signal to noise ratio again. Later, we discuss which filter gave the best result among the five filters. Signal to noise ratio is a measure to know the signal power present to the noise power included. [ABSTRACT FROM AUTHOR]

The entire human race is currently facing a huge disruption of everyday life due to the rapid spread of the novel Corona Virus disease 2019 (COVID-19). It is essential to develop a tool or model for fast diagnosis of the disease which is pandemic and also the model should be able to justify the result for trustworthy in the field of medicine. Machine learning (ML) and Deep Learning (DL) models play a vital role in identifying COVID-19 patients by visually analyzing their Computed Tomography (CT) scan images. In this paper, few publicly available convolutional neural network models (CNN) were analyzed to classify the CT scan images of lungs into two classes, COVID-19 positive and negative cases. In addition to that, Local Interpretable Model-agnostic Explanation (LIME) framework is used as an explanation technique for interpretability. The pixel of relevancy responsible for the outcome of classification is visually explained through LIME technique which gives trustworthiness in the field of healthcare. [ABSTRACT FROM AUTHOR]