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MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images. [ABSTRACT FROM AUTHOR]

Abstract MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images.

BACKGROUND: Anterior cruciate ligament avulsion fracture at tibial insertion is a special type of intra-articular fracture, which is mostly seen in sports injuries. At present, there are relatively few anatomical studies on this disease, but a large number of studies focus on the injury of the anterior cruciate ligament. It is generally believed that the increase in the posterior slope of the tibial plateau, the decrease of the depth of the medial tibial plateau, and the decrease in the width index of the femoral intercondylar notch are the risk factors for the injury of the anterior cruciate ligament. It is unclear whether avulsion fractures of the anterior cruciate ligament at the tibial insertion point are also related to it. OBJECTIVE: To investigate the correlation between the avulsion fracture of the anterior cruciate ligament at the tibial insertion and the posterior tibial slope, the medial tibial depth, the notch width index of the femoral intercondylar fossa, and the coronal slope angle of the tibial plateau. METHODS: A retrospective analysis was made on 101 patients who were admitted to the Department of Orthopedics of Affiliated Hospital of Xuzhou Medical University due to anterior knee pain from January 2019 to December 2022. Totally 51 patients with tibial insertion avulsion fracture of anterior cruciate ligament who received arthroscopy treatment were taken as the observation group, and 50 patients with anterior knee pain but no knee joint injury confirmed by physical examination and imaging examination were taken as the control group in the same period. The posterior tibial slope, anatomical parameters such as the medial tibial depth, and the notch width index of the femoral intercondylar fossa, were statistically analyzed for the anatomical risk factors leading to the tibial insertion avulsion fracture of the anterior cruciate ligament. RESULTS AND CONCLUSION: (1) There was no significant difference between the two groups in the comparison of the lateral posterior tibial slope, the posterior slope ratio of the lateral/medial tibial plateau, the notch width index of the femoral intercondylar fossa, and the coronary slope angle of the tibial plateau (P > 0.05). The medial posterior tibial slope in the observation group was significantly higher than that in the control group (P < 0.05). The medial tibial depth in the observation group was significantly lower than that in the control group (P < 0.05). (2) Multifactor logistic regression analysis showed that the medial posterior tibial slope and the medial tibial depth were independent risk factors for tibial insertion avulsion fracture of anterior cruciate ligament (P < 0.05). (3) The receiver operating characteristic curve shows that the medial posterior tibial slope and the medial tibial depth had certain predictive values for the tibial insertion avulsion fracture of the anterior cruciate ligament. (4) It is indicated that the increased medial posterior tibial slope and the shallower medial tibial depth are the anatomical risk factors for the avulsion fracture of the tibial insertion of the anterior cruciate ligament. [ABSTRACT FROM AUTHOR]

The Alzheimer disease (AD) is a neurologic brain condition, which affects the cells in the brain and eventually renders a patient incapable of performing routine daily tasks. Due to the outstanding spatial clarity, high access, and strong contrast, MRI has been utilized in analyses pertaining to AD. This work develops an AD classification model using MRI images. Here, preprocessing is done by the Gabor filter. Subsequently, the Improved U-net segmentation model is employed for image segmentation. The features extracted comprises of modified LGXP features, LTP features, and LBP features as well. Finally, the Deep ensemble classifier (DEC) model is proposed for AD classification which combines classifiers such as RNN, DBN, and Deep Maxout Network (DMN). For enhancing the efficiency for classification of AD, the optimal weight of DMN is adjusted using the Self Customized BWO (SC-BWO) model. The outputs from DEC are averaged and the final result is obtained. Finally, the analysis of dice, Jaccard scores is performed to show the betterment of the SC-BWO scheme. [ABSTRACT FROM AUTHOR]

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a prevalent kind of syndrome; critical to diagnose in its early stages causes the patient forgets everything in its later stages. In this work, we proposed a design for early diagnosis of Alzheimer's disease; where a multi-class classification system has been implemented which detects AD and classifies the level of disease as Normal, Mild and Severe. The proposed approach starts with mapping the brain's anatomical parts hippocampal, white matter and grey matter and respective volumes are calculated from 3D MRI images. The image segmentation and calculation of volume are done with two software; Analyze Direct and ITK Snap. Calculated volumes of the anatomical parts along with other features like age, gender and MMSE score are fed to different machine learning algorithms for Alzheimer's detection as well as its severity. The extracted features are also fused randomly in all possible ways for further analysis using ML classifiers. The ML algorithms used are random forest, gradient boost, decision tree and KNN. The proposed approach is tested with two sets of data; OASIS dataset and ADNI dataset. Classifier's performance is analyzed based on sensitivity, F1 Score, accuracy and precision for ML classifiers. Random forest is giving the highest accuracy of 99% for white matter volume using OASIS dataset and when all three volumes of hippocampal, white matter and grey matter are fused giving 98% accuracy. For ADNI data set using white matter volume, we are getting 92% accuracy for gradient boost classifier and after fusing all three volumes also getting 92% accuracy. Gradient boost gives an accuracy of around 91% for both databases. [ABSTRACT FROM AUTHOR]

The hippocampal region is one of the most affected brain areas observed as a landmark in Magnetic Resonance Imaging (MRI) images for Alzheimer’s disease (AD) diagnosis. The diminished alterations in the hippocampal and degeneration of cholinergic circuits have been conclusively correlated with a decline in memory and cognitive function. However, the hippocampal region may not appear as clearly defined as other brain regions, making it difficult for neurologists and researchers to identify by visual inspection. The application of deep learning models to pinpoint the hippocampal region was initially valued. We assessed the ability of a deep learning model, You Only Live Once (YOLO), to detect hippocampal regions in three MRI image views and categories. The Alzheimer’s Disease Neuroimaging Initiative-first (ADNI−1) dataset was used with 220 subjects in three categories using the three YOLO models. We obtained the YOLO performance for hippocampal region detection with accuracy in three views and categories. The average mean Average Precision (mAP) performance accuracy for YOLOv3 was 0.87, YOLOv4 was 0.85, and YOLOv5 was 0.96, respectively. The high accuracy of the detection of the hippocampal region was remarkable. We found that the sagittal view was higher than the axial and coronal views. Simultaneously, the Mild Cognitive Impairment (MCI) in the coronal view was lower among the three models. The results showed that YOLOv5 is a suitable model for detecting the hippocampal region in MRI images, and the sagittal view is the most reliable for detecting the hippocampal region in diagnosing AD. Our findings demonstrate the importance of detecting the hippocampal region to diagnose AD and accurately analyzing the hippocampal area within the region. The YOLOv5 model substantially affected performance metrics and interpretability across the three views and categories.

Alzheimer's disease (AD) is a neurodegenerative disorder that leads to the loss of cognitive functions due to the deterioration of brain tissue. Current diagnostic methods are often invasive or costly, limiting their widespread use. Developing non-invasive and cost-effective screening methods is crucial, especially for identifying patients with mild cognitive impairment (MCI) at the risk of developing Alzheimer's disease. This study employs a Machine Learning (ML) approach, specifically K-means clustering, on a subset of pixels common to all magnetic resonance imaging (MRI) images to rapidly classify subjects with AD and those with normal Normal Cognitive (NC). In particular, we benefited from defining significant pixels, a narrow subset of points (in the range of 1.5% to 6% of the total) common to all MRI images and related to more intense degeneration of white or gray matter. We performed K-means clustering, with k = 2, on the significant pixels of AD and NC MRI images to separate subjects belonging to the two classes and detect the class centroids. Subsequently, we classified subjects with MCI using only the significant pixels. This approach enables quick classification of subjects with AD and NC, and more importantly, it predicts MCI-to-AD conversion with high accuracy and low computational cost, making it a rapid and effective diagnostic tool for real-time assessments. [ABSTRACT FROM AUTHOR]

Brain tumors are the second important origin of death worldwide. The early and exact identification of brain tumors is significant for the healing process. With accelerating diagnoses, medicine as well as pricing, quantum computing permits disruptive cases to providers. Quantum improved deep learning was especially significant to the sector. However, the conventional machine learning method faces main challenges to achieve accurate brain tumor detection with MRI images. Therefore, this paper proposes a novel technique called Lee sigma filtered histogram segmentation (LSFHS) for accurately detecting brain tumors with minimal time consumption. LSFHS technique is based on preprocessing, segmentation, feature extraction and classification. Input MRI image is preprocessed using adaptive Lee sigma filter in the first hidden layer to minimize noise significantly. In hidden layer 2, gray bimodal histogram segmentation is performed to partition a preprocessed image into a number of segments. Multiple features are extracted from the input image in the third hidden layer. Output layer uses the TanH activation function to match extracted features with disease features for detecting brain tumors. Experimental evaluation is carried out on factors, namely peak signal-to-noise ratio, tumor detection accuracy, error rate and tumor detection with a number of MRI images. The results illustrate LSFHS technique increases tumor detection accuracy by 14% and 25% faster tumor detection time, and reduces the error rate by 58% compared to state-of-the-art works. Qualitative and quantitative results illustrate that our proposed LSFHS technique attains greater performance than state-of-the-art methods. LSFHS technique is designed to detect brain tumors at an earlier stage with higher tumor detection accuracy and less time. [ABSTRACT FROM AUTHOR]

Accurate image segmentation plays an essential role in diagnosing and treating various spinal diseases. However, traditional segmentation methods often consume a lot of time and energy. This research proposes an innovative deep‐learning‐based automatic segmentation method for spine magnetic resonance imaging (MRI) images. The proposed method DAUNet++ is supported by UNet++, which adds residual structure and attention mechanism. Specifically, a residual block is utilized for down‐sampling to construct the RVNet, as a new skeleton structure. Furthermore, two novel types of dual channel and spatial attention modules are proposed to emphasize rich feature regions, enhance useful information, and improve the network performance by recalibrating the characteristic. The published spinesagt2wdataset3 spinal MRI image dataset is adopted in the experiment. The dice similarity coefficient score on the test set is 0.9064. Higher segmentation accuracy and efficiency are achieved, indicating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Medical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured picture, which might cause distortion, which is unfavourable for the disease diagnosis. Therefore, an appropriate technique for noise suppression is necessary. An attempt has been made to incorporate a two-step filtering based algorithm for noise removal in cardiac MRI images while maintaining the images crucial compositional elements, such as their borders and textures. A combination of adaptive optimum weighted mean filter (AOWMF) and bilateral filter (BF) has been implemented to reduce impulsive noise and Gaussian noise that are both present as mixed noise. The fundamental idea of the AOWMF approach includes the detection of noise pixels using Crow Optimization Algorithm (COA), and replacing them with an optimum weighted mean value depending on a criterion of maximization fitness function. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) have been used to assess the effectiveness of the employed noise reduction technique. The result analysis are experimented in MATLB which reveals that proposed method is able to achieve high PSNR and SSIM and further, this method has the potential to enhance more detail structures of the input cardiac MRI images than existing methods.

The magnetic resonance images focus on soft tissues, and it is often necessary for healthcare professionals to reach the final conclusion in clinical diagnosis. However, these images are often affected by random noise, which decreases the visual quality and reliability of the images. This paper presents an improved unbiased non-local mean (NLM) filter to solve the de-noising issue in the MRI images. Local statistics of the noise is combined with the NLM filter to design an unbiased NLM filter. First of all, the Gaussian noise information is extracted from the noisy image by performing the wavelet decomposition, statistically modeling the diagonal sub-band wavelet coefficients, and estimating the noise variance by applying the median absolute deviation (MAD) estimator. Next, the Rician noise is removed by applying a NLM filter which averages the noisy pixels by a Gaussian weight factor. Finally, the NLM filtered output pixels are unbiased by applying the noise bias subtraction method for recovering the original pixel values. Our experiments on real MRI and synthetic images demonstrate that promising results that can be obtained much superior than results estimated using existing non-local mean filtering schemes. [ABSTRACT FROM AUTHOR]

A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor's location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method's efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%. [ABSTRACT FROM AUTHOR]

Alzheimer's disease has become a major concern in the healthcare domain as it is growing rapidly. Much research has been conducted to detect it from MRI images through various deep learning approaches.However, the problems of the availability of medical data and preserving the privacy of patients still exists. To mitigate this issue in Alzheimer's disease detection, we implement the federated approach, which is found to be more efficient, robust, and consistent compared with the conventional approach. For this, we need deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilize two publicly available datasets (OASIS and ADNI) and design various cases to evaluate the performance of the federated approach. The federated approach achieves better accuracy and sensitivity compared with the conventional approaches in most of the cases. Moreover, the robustness of the proposed approach is also found to be better than the conventional approach. In our federated approach, MobileNet, a low-cost transfer learning architecture, achieves the highest 95.24%, 81.94%, and 83.97% accuracy in the OASIS, ADNI, and merged (ADNI + OASIS) test sets, which is much higher than the achieved performance in the conventional approach. Furthermore, in the proposed approach, only the weights of the model are shared, which keeps the original MRI images in their respective hospital or institutions, preserving privacy in the healthcare domain. [ABSTRACT FROM AUTHOR]

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image. Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%. [ABSTRACT FROM AUTHOR]

BACKGROUND: High-resolution (HR) magnetic resonance imaging (MRI) provides rich pathological information which is of great significance in diagnosis and treatment of brain lesions. However, obtaining HR brain MRI images comes at the cost of extending scan time and using sophisticated expensive instruments. OBJECTIVE: This study aims to reconstruct HR MRI images from low-resolution (LR) images by developing a deep learning based super-resolution (SR) method. METHODS: We propose a feedback network with self-attention mechanism (FNSAM) for SR reconstruction of brain MRI images. Specifically, a feedback network is built to correct shallow features by using a recurrent neural network (RNN) and the self-attention mechanism (SAM) is integrated into the feedback network for extraction of important information as the feedback signal, which promotes image hierarchy. RESULTS: Experimental results show that the proposed FNSAM obtains more reasonable SR reconstruction of brain MRI images both in peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) than some state-of-the-arts. CONCLUSION: Our proposed method is suitable for SR reconstruction of MRI images. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a neurodegenerative disorder that leads to the loss of cognitive functions due to the deterioration of brain tissue. Current diagnostic methods are often invasive or costly, limiting their widespread use. Developing non-invasive and cost-effective screening methods is crucial, especially for identifying patients with mild cognitive impairment (MCI) at the risk of developing Alzheimer’s disease. This study employs a Machine Learning (ML) approach, specifically K-means clustering, on a subset of pixels common to all magnetic resonance imaging (MRI) images to rapidly classify subjects with AD and those with normal Normal Cognitive (NC). In particular, we benefited from defining significant pixels, a narrow subset of points (in the range of 1.5% to 6% of the total) common to all MRI images and related to more intense degeneration of white or gray matter. We performed K-means clustering, with k = 2, on the significant pixels of AD and NC MRI images to separate subjects belonging to the two classes and detect the class centroids. Subsequently, we classified subjects with MCI using only the significant pixels. This approach enables quick classification of subjects with AD and NC, and more importantly, it predicts MCI-to-AD conversion with high accuracy and low computational cost, making it a rapid and effective diagnostic tool for real-time assessments.

Tumours and inflammatory lesions in the brain are common and very dangerous brain diseases. Processing MRI images to detect and distinguish inflammatory lesions from tumours early and quickly will help doctors have timely treatment methods, preventing the disease from getting worse. In this paper, with the MRI image datasets of inflammatory lesions and brain tumours collected from Bach Mai hospital, Vietnam, we developed a complex network with low computational complexity to extract the features of inflammatory lesions and tumours and then use machine learning classification algorithms including SVM, kNN, Random Forest, Logistic Regression, Naive Bayes to distinguish. The performance of the classification algorithms is evaluated through the parameters of accuracy, precision, F1-score, recall and ROC curve. Accuracy achieved by all algorithms is very high in which the highest result is up to 90.26%. This research has successfully applied a complex network with low computational complexity to extract features of MRI images, thereby improving the accuracy in distinguishing inflammatory lesions and tumours in the brain using machine learning algorithms. The rapid and accurate classification of inflammatory lesions and brain tumours is of great significance in screening patients. With such relatively accurate results, in the future, the system can be directly applied in practice. [ABSTRACT FROM AUTHOR]

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In humans, a brain abnormality is a serious illness. Cancer which is the greatest cause of mortality can develop from the tumour. Magnetic resonance imaging (MRI) is among a more extensively utilized medical imaging modalities in brain tumours, then it has become the primary diagnosing mechanism for the treatment and evaluation of brain tumours. Computer‐assisted diagnosis has become a requirement due to the exponential expansion in the quantity of MRIs acquired because of these programmes. Computer‐assisted diagnosis strategies created to increase detection without many systematized readings failed to produce significant improvements in performance measurements. In this regard, the usage of deep learning‐based automatic image processing algorithms appears to be a viable route for identifying brain cancer. In this research, introduce a Cat Swarm Optimization (CSO) algorithm based upon a convolutional neural network (CNN) model utilized to segmentation in a classification of brain tumour. Results of experiments on MRI images using the BRATS dataset show that the CSO algorithm‐CNN model achieved high‐performance in term of 98% of accuracy, precision, specificity, sensitivity and F‐score in the proposed classification task when compared to other classification approaches like support vector machines (SVM) as well as back propagation neural networks (BPNN). [ABSTRACT FROM AUTHOR]

Image segmentation is the method of dividing an image into many segments, comprising groups of pixels. It is a process used to determine objects within the image. Fuzzy c-means (FCM) technique has been popularly employed as grayscale image segmentation method. Meanwhile, the conventional FCM suffers from some drawbacks including easy fall into local optimal solution resulting from inappropriate selection of the initial cluster center values and optimal number of clusters (regions) for each image without a prior knowledge or input by the operator. To solve FCM issues, the paper proposes a new fully automatic segmentation method for grayscale images based on fuzzy c-means with firefly mate algorithm (AUTO-FCM-FMA). This approach utilizes the mate list (M) mechanism with firefly algorithm (FMA) to search for the near-optimal number clusters, the location of centroids by exploring the search space and void stuck in local optimum, and the best outcomes from FMA as input for FCM. To evaluate its effectiveness, the proposed algorithm was tested on different types of images. These images can be categorized into simulated MRI images (normal and MSL), synthetic images and natural images. All these images cover different domains and levels of difficulty (e.g. clusters overlapping). The results of validation experiments were encouraging, especially when the performance of proposed algorithm outcomes was compared to that of other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In the field of medical imaging, accurately classifying brain tumors remains a significant challenge because of the visual similarities among different tumor types. This research addresses the challenge of multiclass categorization by employing Support Vector Machine (SVM) as the core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such as Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP), as well as the dimensionality reduction technique, Principal Component Analysis (PCA). The study utilizes a dataset sourced from Kaggle, comprising MRI images classified into four classes, with images captured from various anatomical planes. Initially, the SVM model alone attained an accuracy(acc&#95;val) of 86.57% on unseen test data, establishing a baseline for performance. To enhance this, PCA was incorporated for dimensionality reduction, which improved the acc&#95;val to 94.20%, demonstrating the effectiveness of reducing feature dimensionality in mitigating overfitting and enhancing model generalization. Further performance gains were realized by applying feature extraction techniques-HOG and LBP-in conjunction with SVM, resulting in an acc&#95;val of 95.95%. The most substantial improvement was observed when combining SVM with both HOG, LBP, and PCA, achieving an impressive acc&#95;val of 96.03%, along with an F1 score(F1&#95;val) of 96.00%, precision(prec&#95;val) of 96.02%, and recall(rec&#95;val) of 96.03%. This approach will not only improves categorization performance but also improves efficacy of computation, making it a robust and effective method for multiclass brain tumor prediction., (© 2024. The Author(s).)

A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Much research has been conducted to detect it from MRI images through various deep learning approaches.However, the problems of the availability of medical data and preserving the privacy of patients still exists. To mitigate this issue in Alzheimer’s disease detection, we implement the federated approach, which is found to be more efficient, robust, and consistent compared with the conventional approach. For this, we need deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilize two publicly available datasets (OASIS and ADNI) and design various cases to evaluate the performance of the federated approach. The federated approach achieves better accuracy and sensitivity compared with the conventional approaches in most of the cases. Moreover, the robustness of the proposed approach is also found to be better than the conventional approach. In our federated approach, MobileNet, a low-cost transfer learning architecture, achieves the highest 95.24%, 81.94%, and 83.97% accuracy in the OASIS, ADNI, and merged (ADNI + OASIS) test sets, which is much higher than the achieved performance in the conventional approach. Furthermore, in the proposed approach, only the weights of the model are shared, which keeps the original MRI images in their respective hospital or institutions, preserving privacy in the healthcare domain.

Brain cancer ranks tenth on the list of leading causes of death in both men and women. Biopsy is one of the most used methods for diagnosing cancer. However, the biopsy process is quite dangerous and take a long time to reach a decision. Furthermore, as the tumor size is rising quickly, non-invasive, automatic diagnostic equipment is required which can automatically detect the tumor and its stage precisely in a few seconds. In recent years, techniques based on Machine Learning and Deep Learning (DL) for detecting and classifying cancers has gained remarkable success in recent years. This paper suggested an ensemble method for detecting and classifying brain tumor and its stages using brain Magnetic Resonance Imaging (MRI). A modified InceptionResNetV2 pre-trained model is used for tumor detection from MRI image. After tumor detection, a combination of InceptionResNetV2 and Random Forest Tree (RFT) is used to determine the cancer stage, which includes glioma, meningioma, and pituitary cancer. The size of the dataset is small, so C-GAN (Cyclic Generative Adversarial Networks) is used to increase the dataset size. The experiment results demonstrate that the suggested tumor detection and tumor classification models achieve the accuracy of 99% and 98%, respectively. [ABSTRACT FROM AUTHOR]

Image classification is a significant way in the field of image processing to automatically categorize large numbers of images. Brain tumor classification is mainly a helpful and widely desired process in the medical system. Brain tumor classification is a significant way to automatically categorize brain tumors images. Many methods have been introduced for solving the classification task with leverage low-level features. However, it has few limitations for achieving the higher accuracy of image tumor classification with minimum time. To overcome the issue, this study has proposed a novel technique called Regularized Anisotropic Filtered Tanimoto Indexive Deep Multilayer Perceptive Connectionist Network (RAFTIDMPCN), consisting of many layers of nodes for deeply analyzing the input and providing better classification results. The proposed architecture helps in improving the accuracy and reducing the time. The input layer receives the number of MRI images and natural image datasets collected from the dataset. Then the images are sent to the first hidden layer where the preprocessing is carried out to improve the image quality by removing the noise pixels using Regularized Anisotropic diffusion filtering technique. Followed by, shape, color, texture, and size features of input images are extracted in the second hidden layer. The classification is performed at the third hidden layer based on the Tanimoto similarity measure. Finally, the Heaviside step activation function is applied to obtain the classification results with higher accuracy. Experimental evaluation is carried out with different qualitative and quantitative results discussion by using brain tumor MRI dataset and natural image datasets. The obtained results indicate that the proposed technique provides better results in terms of Peak signal to noise ratio, accuracy, false-positive rate, time complexity, and space complexity. The analyzed results show the superior performance of our proposed RAFTIDMPCN technique accuracy by 6%, minimizes the false-positive rate by 40%, and time complexity by 11% in brain tumor detection when compared with the two state-of-the-art methods. This paper also presents several discoveries that could be helpful to the neurological community.

Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not only does it require a large amount of work, it is also a complicated task to outline the lesion area in an image manually, using traditional methods. With the development of computer-aided diagnostic techniques, more and more researchers are focusing on automatic segmentation techniques for osteosarcoma analysis. However, existing methods ignore the size of osteosarcomas, making it difficult to identify and segment smaller tumors. This is very detrimental to the early diagnosis of osteosarcoma. Therefore, this paper proposes a Contextual Axial-Preserving Attention Network (CaPaN)-based MRI image-assisted segmentation method for osteosarcoma detection. Based on the use of Res2Net, a parallel decoder is added to aggregate high-level features which effectively combines the local and global features of osteosarcoma. In addition, channel feature pyramid (CFP) and axial attention (A-RA) mechanisms are used. A lightweight CFP can extract feature mapping and contextual information of different sizes. A-RA uses axial attention to distinguish tumor tissues by mining, which reduces computational costs and thus improves the generalization performance of the model. We conducted experiments using a real dataset provided by the Second Xiangya Affiliated Hospital and the results showed that our proposed method achieves better segmentation results than alternative models. In particular, our method shows significant advantages with respect to small target segmentation. Its precision is about 2% higher than the average values of other models. For the segmentation of small objects, the DSC value of CaPaN is 0.021 higher than that of the commonly used U-Net method. [ABSTRACT FROM AUTHOR]