Search

Recently, huge concerns have been raised in diagnosing chest diseases, especially after the COVID-19 pandemic. Regular diagnosis processes of chest diseases sometimes fail to distinguish between Corona and Viral Pneumonia diseases through Polymerase Chain Reaction (PCR) tests which are a time-engrossing process that needs convoluted manual procedures. Artificial Intelligence (AI) techniques have achieved high performance in aiding medical diagnostic processes. The innovation of this work lies in using a new diagnostic technique to distinguish between COVID-19 and Viral Pneumonia diseases using advanced AI technologies. This is done by extracting novel features from chest X-ray images based on Wavelet analysis, Scale Invariant Feature Transformation (SIFT), and the Mel Frequency Cepstral Coefficient (MFCC). Support vector machines (SVM) and artificial neural networks (ANN) were utilized to build classification algorithms using 1200 chest X-ray mages for each case. Using Wavelet features, the results of evaluating the SVM and ANN models were 97% accurate, and with SIFT features, they were closer to 99%. The proposed models were very effective at identifying COVID-19 and Viral Pneumonitis, so physicians can determine the best treatment course for patients with the support of this high accuracy. Moreover, this model can be used in hospitals and emergency rooms when a massive number of patients are waiting, as it is faster and more accurate than the regular diagnosis processes as each step takes few seconds on average to complete. [ABSTRACT FROM AUTHOR]

Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application's adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%. [ABSTRACT FROM AUTHOR]

Retinal image segmentation process deals with problems like spurious vascularisation and thin vessel detection. In this paper, a three-step methodology has been proposed for retinal vessel segmentation. In first step, RGB to YIQ conversion is performed. In second step, Y component enhancement is performed. A novel Chaotic weighted Elephant Herding Optimization (CWEHO) has been proposed to optimize the clip limit and block size values of Contrast Limited Adaptive Histogram Equalization (CLAHE). CWEHO-based CLAHE along with morphological operations, non-local means filter, and median filter is applied to enhance retinal images. In third step, thin and thick vessel segmentation is performed. Top hat transformation, otsu thresholding algorithm, and vessel point selection are applied for thick vessel extraction. The first-order Gaussian derivative in conjunction with the match filter is used to extract thin vessels. DRIVE and HRF datasets are used to assess the effectiveness of proposed methodology. The average values of segmentation accuracy, specificity, sensitivity, and Mathew Correlation Coefficient (MCC) are observed to be 0.9650, 0.9895, 0.7007, 0.7650, respectively, for observer1 and 0.9696, 0.9912, 0.7390, 0.7901 for observer2 using DRIVE dataset. Similarly, 0.9592, 0.9839, 0.6850, and 0.7116, respectively, metrics for HRF dataset. Compared to state-of-the-art methods, the proposed segmentation methodology provides better results. [ABSTRACT FROM AUTHOR]

Image muscle segmentation is useful to quantitatively assess musculoskeletal diseases by extracting biomarkers such as shape, texture and water diffusivity metrics. Although volumetric manual segmentation is time consuming and a bottleneck in practice, fully automatic approaches are still in progress to reach an acceptable accuracy. In this paper, we provide a robust semi-automated tool to segment two musculoskeletal systems, i.e. thigh and shoulder in MRI and CT modalities, respectively. The tool only needs a few manually labelled cross-sections to build a directed graph-structure of corresponding points between the successive spaced slices. The boundaries of each muscle are obtained by performing a spline interpolation based on the directed graph-structure. Each muscle label and its corresponding 3D mesh are deduced using post-processing techniques. We evaluated the tool on 26 MRI thighs and 16 CT shoulders. Three metrics along with inter-muscle overlapping were employed to evaluate the tool by comparison to an expert manual segmentation and a publicly available tools (ITK-SNAP, 3D Slicer). The results showed a mean Dice $0.988 \pm 0.003$ 0.988 ± 0.003 , and Hausdorff Distance $4.86 \pm 1.67$ 4.86 ± 1.67 mm in comparison to the manual reference for thigh muscle segmentation, and a mean Dice $0.961 \pm 0.005$ 0.961 ± 0.005 and Hausdorff Distance $2.42 \pm 0.79$ 2.42 ± 0.79 mm for shoulder muscle segmentation, outperformed the other methods. The tool is proposed as slicer module available at . [ABSTRACT FROM AUTHOR]

One of the common eye conditions affecting patients with diabetes is diabetic retinopathy (DR). It is characterised by the progressive impairment to the blood vessels with the increase of glucose level in the blood. The grading efficiency still finds challenging because of the existence of intra-class variations and imbalanced data distributions on the retinal images. Traditional machine learning techniques utilise hand-engineered features for classification of the affected retinal images. As convolutional neural network produces better image classification accuracy in many medical images, this work utilises the CNN-based feature extraction method. This feature has been used to build Gaussian mixture model (GMM) for each class that maps the CNN features to log-likelihood dimensional vector spaces. Since the Gaussian mixture model can be realised as a mixture of both parametric and nonparametric density models and has their flexibility in capturing different data distributions, probabilistic outputs, interpretability, efficient parameter estimation, and robustness to outliers, the proposed model aimed to obtain and provide a smooth approximation of the underlying distribution of features for training the model. Then these vector spaces are trained by the SVM classifier. Experimental results illustrate the efficacy of the proposed model with accuracy 86.3% and 89.1%, respectively. [ABSTRACT FROM AUTHOR]

Canine Hip Dysplasia (CHD) is a congenital disease with a polygenic hereditary component, characterised by abnormal development of the coxo-femoral joint which results in poor coaptation of the femoral head in the acetabulum; the disease rapidly progresses to osteoarthritis of the hip. While dysplasia has been recognised in practically all canine breeds, it is much more common and of concern in medium and large dog breeds with rapid development. Dysplasia in predisposed breeds, particularly the German Shepherd, is the object of screening based on systematic radiological control in some countries. Our collected dataset comprises 507 X-ray images of dogs affected by hip dysplasia (HD). These images were meticulously evaluated using six Deep Convolutional Neural Network (CNN) models. Following an extensive analysis of the top-performing models, VGG16 emerged as the leader, achieving remarkable accuracy, recall, and precision scores of 98.32%, 98.35%, and 98.44%, respectively. Leveraging deep learning (DL) techniques, this approach excels in diagnosing CHD from hip X-rays with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Markerless motion capture has been made available by the development of pose estimation algorithms that provide both an estimate of the location of body keypoints in two-dimensional images and its associated confidence. It seems relevant to use this additional information for three-dimensional (3D) point reconstruction. Yet, it has been little described, nor has its influence on 3D point reconstruction. Eight participants performed a manual material handling task, which was recorded by 10 video cameras. Each video was processed using OpenPose. Different 3D point reconstruction methods were compared: direct linear transform (DLT) and weighted DLT (wDLT), with 10 cameras and with a subset of 4 cameras. For each keypoint, confidence and position deviation from the 3D reconstructed point with 10 cameras projected in the image reference frame were assessed. Results suggest that using confidence information reduces both average and maximum 3D distance between 3D points reconstructed with 4 and 10 cameras. [ABSTRACT FROM AUTHOR]

Setting up a complex CNN requires a powerful platform, several hours of run-time, and a lot of data for training. Here, we propose a generalised lightweight solution that exploits super-resolution and scalable vector graphics and uses a small-scale UNet as the baseline framework to segment different organs in MR and CT data. We selected the UNet since many researchers use it as the baseline, modify it in their proposal, and perform an ablation study to show the effectiveness of the proposed modification. First, we downsample the input 2D CT slices by bicubic interpolation. Using the architecture of the conventional UNet, we reduce the size of the network's input, and the number of layers and filters to construct a lightweight UNet. The network segments the low-resolution images and prepares the mask of an organ. Then, we upscale the boundary of the output mask by the Scalable Vector Graphics technique to obtain the final border. This design reduces the number of parameters and the run-time by a factor of two. We segmented several tissues to prove the stability of our method to the type of organ. The experiments proved the feasibility of setting up complex deep neural networks with conventional platforms. [ABSTRACT FROM AUTHOR]

Facial Emotion Recognition is gaining interest from researchers in various fields due to its numerous applications. The Vision Transformer (ViT) outperforms convolutional neural network (CNN)-based systems significantly in terms of performance when compared to contemporary image categorisation algorithms. In this context, the primary objective of this paper is to present an innovative and efficient deep learning model for facial emotion recognition. This hybrid intelligent model combines the Vision Transformer with Particle Swarm Optimization (PSO). The proposed model accurately identified the majority of emotions in images, achieving an accuracy of 95.93% for the Fer2013plus dataset and 100% for the CK+ dataset, as demonstrated through experiments conducted on both datasets. According to the results, the proposed facial expression recognition approach has either outperformed or closely matched the performance of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper introduces a Computer-Aided Detection (CAD) system for categorizing breast masses in mammogram images from the DDSM database as Benign, Malignant, or Normal. The CAD process involves Pre-processing, Segmentation, Feature Extraction, Feature Selection, and Classification. Three feature selection methods, namely the Genetic Algorithm (GA), t-test, and Particle Swarm Optimization (PSO) are used. In the classification phase, three machine learning algorithms (kNN, multiSVM, and Naive Bayes) are explored. Evaluation metrics like accuracy, AUC, precision, recall, F1-score, MCC, Dice coefficient, and Jaccard coefficient are used for performance assessment. Training and testing accuracy are assessed for the three classes. The system is evaluated using nine algorithm combinations, producing the following AUC values: GA+kNN (0.93), GA+multiSVM (0.88), GA+NB (0.91), t-test+kNN (0.91), t-test+multiSVM (0.86), t-test+NB (0.89), PSO+kNN (0.89), PSO+multiSVM (0.85), and PSO+NB (0.86). The study shows that the GA and kNN combination outperforms others. [ABSTRACT FROM AUTHOR]

Medical imaging research has experienced significant growth over the past decade, particularly in the fields of computer vision and pattern recognition. Computational approaches have been proposed to address the challenges in breast cancer detection, classification and segmentation. However, recent advancements in computational technology such as machine learning (ML) methods have dramatically changed the landscape of breast cancer imaging research. This survey aims to provide a compilation of information for future breast cancer imaging researchers by 1) comprehensively examining how various ML techniques are being used to address the main challenges in breast cancer imaging; 2) providing an in-depth discussion and review of publicly available datasets for the development and evaluation of novel breast cancer detection, classification and segmentation approaches; and 3) outlining current evaluation metrics used by breast cancer imaging researchers. The insights and findings presented in this survey will serve as a valuable resource for researchers and clinicians interested in breast cancer imaging. By providing an overview of the current state-of-the-art techniques and highlighting areas for future research, we hope to facilitate the development of more accurate and effective breast cancer imaging ML techniques, and contribute to advancing our understanding and improving the diagnosis and treatment of breast cancer. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is a dynamic field intersecting various disciplines, with applications spanning medicine and diagnostics. Our focus on advancing medical imaging has led to groundbreaking progress in brain tumor diagnosis, fostering optimism in treatment. In this pursuit, we introduce HoloBrain, an accessible AR application designed to transform medical education. Utilizing smartphones, HoloBrain enables automatic segmentation, AR visualization, and 3D interaction without the need for head-mounted devices. Emphasizing practicality over hyper-realism, our innovation allows medical students to seamlessly integrate a virtual brain tumor model into their surroundings. This immersive approach facilitates exploration of the 3D representation of an afflicted brain, enhancing educational experiences. Tests in a controlled environment using a phantom head yielded promising results, with our solution demonstrating average errors of 2 mm to 4.5 mm on smartphone displays. These outcomes highlight the precision and effectiveness of our contribution, underlining its potential in transforming medical education and training. HoloBrain represents a step forward in making AR accessible and impactful for aspiring medical professionals, offering a valuable tool for enhancing their understanding of complex medical scenarios. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancements in deep learning, image processing has progressively attracted the attention of medical researchers to achieve accurate diagnoses of numerous human diseases in a non-invasive manner. Image processing systems are among the most helpful and crucial disease identification technologies. Liver cancer is the world's fifth most prevalent and leading cause of cancer deaths. Diagnoses and treatment are essential because of the high mortality rate and recurrence after treatment. Medical image analysis is extremely beneficial in the healthcare industry. In the last several years, creating and developing computer-assisted imaging systems has been increasingly vital to aid physicians or doctors in strengthening their diagnoses. This research presents a deep model to identify cancerous regions in the liver aims using Computerized Tomography (CT) scans from the Kaggle dataset. Basic preprocessing is applied to CT images from the dataset acquired from the Kaggle repository. The performance of the proposed model is evaluated using different performance metrics, and an accuracy score of 94.3% was achieved in tumour identification. [ABSTRACT FROM AUTHOR]

This study aims to tackle the implementation of a single deep learning (DL) technique to the brain tumour recognition, segmentation and classification problem based on MR images. We propose utilising the Mask R-CNN approach along with transfer learning models on images. This was done by assigning bounding boxes and successfully constructing a border on each image for the tumour volume in order to distinguish it from neighbouring tissues and structures. By processing the images in this way, a single DL model could accurately identify, classify and segment three different categories of brain tumours (meningioma, glioma or pituitary). ResNet-50 and ResNet-101 network architectures were trained for 1000 epochs, and their classification and segmentation performances were assessed. A higher classification success was achieved for the ResNet-101 backbone pretrained on COCO images (%75 accuracy) in the testing phase. ResNet-50, on the other hand, achieved a higher classification accuracy (%87) when used with the ImageNet dataset. The classification and segmentation successes of meningioma and pituitary tumours were comparable, while glioma tumours could be segmented at relatively lower success rates (41–73% for all models), despite similar classification performances (73–95%) with other tumour types. [ABSTRACT FROM AUTHOR]

A brain tumour is fatal; thus, early discovery is crucial to preserving life. Brain magnetic resonance image (MRI) segmentation can be used to find the tumour area. Radiologic assessments can pinpoint a suspected brain tumour's exact location and size. For future diagnostic and treatment planning, the assessment report is crucial. For the goal of diagnosis, the tumour detection process must be quick and precise. This paper suggests an automated system to distinguish between malignant brain MRIs and those not. Each image is assigned a set of characteristics utilising statistical, edge. Then, to compare the precision of the suggested framework, the Extreme Learning Machine (ELM) classifier is used with various cross-validations on the feature set. The BRATS 2020 dataset is used to validate the suggested technique. The approach has an average accuracy, sensitivity, and specificity of 99.1%, 99.9%, and 99.3%. It can detect tumours more precisely and with less processing time than current techniques. This study suggests a powerful method for identifying brain cancers and predicting the kind of tumour. The suggested method first uses the adaptive histogram equalisation strategy to boost the resolution of BRATS-2020 MRI images. The goal is to improve segmentation performance as a result. After that, the WFCM technique is used to segment the MRI picture. [ABSTRACT FROM AUTHOR]

This work uses Fuzzy Lattices and Neural Reinforcement Learning techniques for seizure classification. EEG database of Bonn University and CHB-MIT has been used for the evaluation of the proposed method. Here, features are represented in the form of fuzzy lattices, and feature vectors are created in the form of Kinetic Energy (K.E.) using Schrödinger equation. Then, the highest K.E.-based seven fuzzy lattices have been used for classification using Neural Reinforcement Learning classifier. Neural Reinforcement Learning classifier is a self-learner method that classifies different seizure sub-classes (healthy eyes open, healthy eye closed, Epileptogenic Zone, hippocampal formation of opposite hemisphere, epileptic seizure). The effectiveness of the proposed method has been tested on two different public datasets. The average classification accuracy achieved is 97.6% and 97.5% for Bonn and CHB-MIT datasets, respectively. Results are compared with existing techniques to show the precedence of proposed approach. Also, computation speed of proposed classifier is more than 1.5 times compared to Fuzzy Q Learning classifier. The objective is to develop a hybrid model integrating fuzzy lattices and neural reinforcement learning (adaptive methods) for accurate classification, aiming to enhance and speeding up seizure detection and diagnosis to improve patient care through advanced computational techniques. [ABSTRACT FROM AUTHOR]

This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of 'Neural Network (NN) and Support Vector Machine (SVM)' for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively. [ABSTRACT FROM AUTHOR]

Attention is a cognitive process that is essential for human performance. Human development frequently depends on attention; however, this topic still requires additional research. Recently, EEG brain waves have been utilised to identify a person's attention states. This work aims to review numerous machine learning algorithms to analyse the Electroencephalographic (EEG) data to recognise human attention. Various machine learning approaches for the analysis of emotional states with EEG data were reviewed. Moreover, the analysis includes the performance achieved in various works; their benefits and disadvantages are reviewed. An EEG data processing pipeline and a review of human attention recognition are developed to evaluate the performance of various machine learning techniques. According to the analysis, the neural network framework achieves 99.81% accuracy. The results serve as a design framework for future systems using EEG data on brain activity to monitor people's health. [ABSTRACT FROM AUTHOR]

This paper has aimed to develop the Breast Cancer Detection (BCD) technique along with the mammography reports of the patients. For this proposed framework, the required images are collected from publicly available datasets. Further, the collected images are given into the phase of feature extraction, where the deep features based on texture features, ResNet, morphological features and density features are extracted and fused together. In addition, the weighted fused feature selection is performed with the fused features, in which the weight is optimised with the Modified Hybrid based Leader Optimization (MHLBO) obtained from Hybrid Leader-Based Optimization(HLBO) to elevate the feature performance on classification. Then, breast cancer can be classified with the developed Adaptive Cascaded Deep Network (ACDN) by combing the classification results of, Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). Here, the parameter tuning has occurred in all three classifiers using the same enhanced HLBO. Throughout the experimentation, the designed model shows 97% and 96% regarding accuracy and F1-score, respectively. The tentative outcomes show the efficacy of the designed BCD method is compared with other baseline approaches. [ABSTRACT FROM AUTHOR]

Corneal Ulcers are defined as inflammation or even infection. They are one of the most frequent diseases that affect eye health. The proper measurement of corneal ulcer lesions enables the physician to evaluate the treatment effectiveness and decision making. This paper presents a segmentation method that aims to assist doctors in monitoring the treatment of corneal ulcer lesions. We applied a stepwise fine-tuning in U-Net Convolutional Neural Network architecture to train a model with 358 Point-flaky corneal ulcer images. The result from the model using U-Net architecture is then submitted to post-processing operations. Based on experiments performed with 91 Flaky corneal ulcer images, our approach achieved 0.823 of the Dice Coefficient on average, 88.9% of Recall, 99.4% of Specificity, and True Dice Coefficient of 0.835. The results are promising; we then have evidence that the proposed method could generalise the data from the training phase to segment the test data. [ABSTRACT FROM AUTHOR]

The Multimodal Medical Image Fusion (MMIF) is affected by poor image quality, which leads to the extraction of inefficient features. The main intent of this work is to fuse various planes in the PET and MRI medical images efficiently using the MMIF approach. Initially, the sample images containing the axial plane of PET and MRI images are aggregated from standard datasets. Then, the collected images are employed for the decomposition process, which is accomplished via Optimal Non-Subsampled Contourlet Transform (ONSCT). The parameters in the NSCT are optimized using the Modified Water Strider Algorithm (MWSA. Once the images are decomposed, it is segmented into two sub-bands as high frequency and low-frequency sub-bands. Consequently, the high-frequency sub-bands of both PET and MRI images are fused by using the optimal weighted average fusion, in which the weight factor is obtained optimally by the MWSA algorithm. Similarly, the low-frequency sub-bands of both medical images are combined by sparse fusion technique. Finally, both the resultant fused images are subjected to Inverse Non-Subsampled Contourlet Transform (INSCT) to get desired fused images. The experimental findings suggest that the proposed model has effectively fused the images, and it also enhances the similarity score with axial planes. [ABSTRACT FROM AUTHOR]

Some of the viruses may cause lung parenchyma and airway involvement. Usually, viral pneumonia causes ground-glass opacities, bilateral peripheral distribution, consolidation, vascular thickening, and reticular opacity. These features are common in COVID-19 rather than Non-Covid-19 viral pneumonia. However, in advanced cases, COVID-19 viral pneumonia may cause organising pneumonia and fibrosis of the lung. Atypical findings of Non-Covid-19 pneumonia have included central peripheral distribution, pleural effusion, lymphadenopathy, nodules, tree-in-bud opacities, and pneumothorax. Therefore, differentiating Non-Covid-19 pneumonia from COVID-19 pneumonia at chest computed tomography (CT) is necessary. In that case, CT scans of the thorax are one of the essential tools for early identification and future prognosis of viral pneumonia. We have proposed a Computer-Aided Diagnostic (CAD) system that can detect features of chest CT using a Deep Neural Network (DNN) with Long Short-Term Memory (LSTM). Transfer learning using pre-trained DNN models (ResNet50, VGG19, InceptionV3, Xception, DenseNet121, and VGG16) is applied to retain both high-level and low-level features effectively. The deep features are passed to the LSTM layer. The LSTM is utilised as a classifier and detects long short-term dependencies. The proposed method employs a hybrid DNN-LSTM network for automatic detection to take advantage of the uniqueness of the two models. The proposed models are trained with common and different features present in the chest CT of COVID-19 and Non-Covid-19 viral pneumonia. The 5-fold cross-validation (CV) method validated and tested the proposed model. The proposed DNN model's performance is quite improved with LSTM and CV. As a result, the proposed LM-DNN (VGG16+LSTM+CV) model has achieved the classification test accuracy of 91.58% and specificity of 93.86%, which offers superior performance with state-of-the-art. Also, the DenseNet121+LSTM+CV model has reached the classification test accuracy of 90.1% and sensitivity of 92%. [ABSTRACT FROM AUTHOR]

Upper-limb prostheses are either too expensive for many consumers or have a greatly simplified choice of actions. This research aims to enable an improvement to the quality of life for recipients of these devices by providing an understanding of how we use our hands in modern everyday life. To achieve this a taxonomy of grasps used in activities of daily living has been created. A portable motion capture system was developed to collect data. Thirteen participants' hand movements were recorded (totalling 62 hours and 10 minutes of data). From these data, 38 and 22 grasps were observed from the left and right hands, respectively. The portable system effectively captured natural hand motions, which formed an updated taxonomy of grasps. [ABSTRACT FROM AUTHOR]

Skin melanoma is a potentially fatal form of cancer. If left untreated or allowed to spread, it can lead to death or serious disability. Therefore, early diagnosis is crucial for improving patient prognosis and outcomes. Recent advances in machine learning (ML) and deep learning (DL) have greatly contributed to the categorisation and identification of melanoma. The goal of this survey is to evaluate 60 publications that have been submitted in order to create an overview of the melanoma detection process. It examines several feature extraction techniques.The evaluation concentrates on different melanoma detection methods, including 'deep learning (DL)' and 'machine learning (ML)' models. The analysis includes performance metrics and a review of the results obtained from the PH2 dataset, which achieved a high accuracy of 96.5%. Finally, the survey addresses research gaps to facilitate future investigations into melanoma identification strategies. [ABSTRACT FROM AUTHOR]

A virus known as the Coronavirus 2019 (COVID-19) can have serious effects on a variety of body parts. Preventing this spread of the virus requires early detection and proper treatment. Many methods are followed for early detection of this disease based on symptoms and classification for proper treatment. In this paper, classification of COVID-19 using Computed Tomography (CT) scan images with hybrid optimisation-enabled deep learning is researched. Here, a hybrid optimisation algorithm called War Snake Optimisation Algorithm (WSOA) is proposed to train Deep Q-Network (Deep Q-Net). Moreover, the proposed WSOA is formed by integrating War strategy optimisation (WSO) and Snake Optimiser (SO). In this paper, pre-processing is carried out using a median filter, followed by lung lobe segmentation that is done by Generative Adversarial Network (GAN). Furthermore, image augmentation is done by translation, rotation, flipping and scaling. Then, feature extraction is followed after image augmentation, where needed features get extracted to end up with the classification of COVID-19. This classification brings out the patient's health condition of normal and abnormal situations. Moreover, this research work is analysed with three performance metrics, such as accuracy, sensitivity and specificity with values of 93.7%, 94.8% and 89.5%. [ABSTRACT FROM AUTHOR]

A method of enhancing colour fundus photographs is proposed to reduce the effect of cataracts. The enhancement method employs a decorrelation stretch (DS) technique in an LCC colour model. The initial designed technique embeds Hubbard's colouration model into DS parameters to produce enhanced results in a standard form of age-related macular degeneration (AMD) reading centres. The colouration model could modify to enhance the colour of lesions observed in diabetic retinopathy (DR). The proposed algorithm could improve the effect of cataracts on fundus images and provided good results when the density of the cataract was less than grade 2. In the case of images taken through cataracts higher than or equal to grade 2, some output results could become unusable when the cataract was in line with the macula. [ABSTRACT FROM AUTHOR]

COVID-19 has seriously affected normal life as well as public safety. It is extremely transmissible and has now infected millions of people worldwide. To obtain more image features of the lungs, Computed Tomography (CT) scans are widely used. However, manual examination of CT images for abnormal areas of COVID-19 disease can be time-consuming, and it is highly subjective to determine whether they are infected. To rapidly screen patients, Machine Learning (ML) can be used to determine whether patients have the disease. In this paper, a hybrid extraction technique is used to extract feature vectors from CT images, which is a mixture of a histogram of orientation gradients (HOG) extraction technique and a local binary pattern (LBP) extraction technique. In this experiment, 960 NON-COVID-19 and 960 COVID-19 were adopted to train the model, and 240 NON-COVID-19 and 240 COVID-19 were used to test the model. And the CT images were scaled to a uniform size. After obtaining the feature vectors using HOG and LBP feature extraction methods, The CT images were classified using a Support Vector Machine (SVM) classifier optimised by Particle Swarm Optimisation (PSO). In the performance evaluation of the presented classification model, the combination of the HOG feature extraction technique and the LBP feature extraction technique resulted in a substantial improvement in the classification effectiveness of the SVM. HOG_LBP PSO SVM improved Accuracy to 97.5%, Precision to 97.75%, Recall to 97.27%, Specificity to 97.25%, F1_score to 97.50%, and Mcc to 95.01%. [ABSTRACT FROM AUTHOR]

The liver is the largest solid organ in the body that can be anatomically divided into segments. We present in this work a web-based subject-specific human liver atlas based on the Couinaud segments simulated from portal venous (PV) perfusion zones, hepatic arterial (HA) and hepatic venous (HV) trees, as well as biliary drainage. The purpose of the atlas is to provide the modelling community with freely accessible 3D hepatic structures for in silico simulations, which are of tremendous value in yielding novel insights in hepatic circulation, drug transport and clearance. [ABSTRACT FROM AUTHOR]

This study utilised a prototype system which consisted of a person-specific 3D printed jaw tracking harness interfacing with the maxillary and mandibular teeth and custom jaw tracking software implemented on a smartphone. The prototype achieved acceptable results. The prototype demonstrated a static position accuracy of less than 1 mm and 5°. It successfully tracked 30 cycles of a protrusive excursion, left lateral excursion, and 40 mm of jaw opening on a semi-adjustable articulator. The standard error of the tracking accuracy was reported as 0.1377 mm, 0.0449 mm, and 0.9196 mm, with corresponding ${r^2}$ r 2 values of 0.98, 1.00, and 1.00, respectively. Finally, occlusal contacts of left, right, and protrusive excursions were tracked with the prototype system and their trajectories were used to demonstrate kinematic modelling (no occlusal forces) with a biomechanical simulation tool. [ABSTRACT FROM AUTHOR]

Machine learning methods have been extensively employed to predict COVID-19 using chest X-ray images in numerous studies. However, a machine learning model must exhibit robustness and provide reliable predictions for diverse populations, beyond those used in its training data, to be truly valuable. Unfortunately, the assessment of model generalisability is frequently overlooked in current literature. In this study, we investigate the generalisability of three classification models – ResNet50v2, MobileNetv2, and Swin Transformer – for predicting COVID-19 using chest X-ray images. We adopt three concurrent approaches for evaluation: the internal-and-external validation procedure, lung region cropping, and image enhancement. The results show that the combined approaches allow deep models to achieve similar internal and external generalisation capability. [ABSTRACT FROM AUTHOR]

Brain injuries caused by strokes are one of the leading causes of disability worldwide. Current procedures require a specialised physician to analyse MRI images before diagnosing and deciding on the specific treatment. However, the procedure can be costly and time-consuming. Artificial intelligence techniques are becoming a game-changer for analysing MRI images. This work proposes an end-to-end approach in three stages: Pre-processing techniques for normalising the images to the standard MNI space, as well as inhomogeneities and bias corrections; lesion segmentation using a CNN network, trained for cerebrovascular accidents and feature extraction; and, classification for determining the vascular territory within which the lesion occurred. A CLCI-Net was used for stroke segmentation. Four Deep Learning (DL) and four Shallow Machine Learning (ML) network architectures were evaluated to assess the strokes' territory localisation. All models' architectures were designed, analysed, and compared based on their performance scores, reaching an accuracy of 84% with the DL models and 95% with the Shallow ML models. The proposed methodology may be helpful for rapid and accurate stroke assessment for an acute treatment to minimise patient complications. [ABSTRACT FROM AUTHOR]

Climate and LULC changes have a great influence on the hydrological processes which include evapotranspiration, infiltration, surface runoff, groundwater flow, and stream discharge regime. This study aims to review the existing literature on the impacts of climate, and LULC changes on the sustainability of irrigation water in West Africa. The future of irrigation in West Africa will be driven by strong external factors, such as rapid population growth, climate change, and LULC change. The annual average temperature is between 25.9 and 34°C. Rising temperatures have affected all sectors, including water resources, increased irrigation demand, and the sustainability of agricultural production. Reduction in precipitation will reduce river flows and increase evaporation due to a drier atmosphere, thereby reducing the amount of water available in reservoirs for irrigation. The current demand for freshwater in West Africa for irrigation purposes is expected to triple by 2050. From 1997 to 2018, forest area decreased by 24.6%, while residential and agricultural land areas increased by 140% and 11.7%, respectively, in West Africa. Effective mitigation and adaptation measures are the policy issue for the observed adverse impacts of future climate and LULC changes on the sustainability of irrigation water availability in West Africa. [ABSTRACT FROM AUTHOR]

In large-scale debris flow susceptibility assessments, there is often excessive manual intervention, low efficiency, and inadequate model accuracy. To address these issues, this paper integrates multiple data sources and proposes a Multi-channel and Multi-scale Residual Network (MMRNet) for automatic extraction of gully features. Firstly, MMRNet employs a multi-scale feature fusion module to capture both local and global features of gullies, enhancing the model's feature representation capabilities. It then uses an improved residual structure to fuse shallow features, compress features, and improve assessment efficiency. Additionally, channel rearrangement techniques are used to enhance feature flow. Finally, susceptibility prediction is made based on the similarity between the gully under evaluation and gullies where debris flows have occurred. The natural breakpoint method is used to classify susceptibility results into five levels. Experimental results show that the very high susceptibility zones for debris flows are mainly concentrated in areas with abundant river systems along the Nujiang River, covering 61.68% of the entire study area, with a debris flow proportion of 98.78%. The MMRNet model achieves an accuracy (ACC) of 81.6% and an area under the curve (AUC) of 0.8320, indicating that this model is a high-performance method for debris flow susceptibility assessment. [ABSTRACT FROM AUTHOR]

Mastering the changes in the planting area of crops are critical for formulating sustainable agricultural development strategies. The Landsat 9 satellite provides continuous observational data for crop mapping. It is necessary to evaluate the potential of Landsat 9 images for canola mapping. According to literatures, some canola indices have been proposed in relation to the spectral features of the yellow canola flower, such as the canola ratio index (CRI), normalised difference yellowness index (NDYI), canola index (CI), canola flower index (CFI), normalised difference rapeseed flower index (NRFI) and winter rapeseed index (WRI). However, it is unclear which index has more potential to extract canola based on the Landsat 9 images. Therefore, the potentials of these indices for canola mapping were quantitatively compared by using different classification methods including supervised and unsupervised classification methods. For supervised classification methods and the CFI, the overall accuracy of canola mapping was above 90% and the kappa coefficient was above 0.8. For unsupervised classification methods, CFI also performs best. It was demonstrated that the CFI outperformed the other five indices for canola mapping. It was also confirmed that Landsat 9 images and the CFI exhibit promising potential for canola mapping via quantitative comparison. [ABSTRACT FROM AUTHOR]

Topographic influences of the ground motions in 3D geological configurations have been addressed using Spectral Element Method as implemented in SPECFEM3D code developed to simulate seismic wave propagation. Hexahedral mesh model was constructed to which the 1987 Jinka earthquake source characteristics were applied. The simulation results have shown that topography has significant effects on ground motion and changes PGA values by ± 55% compared to the flat topography. PGA amplification variation between valley and hill/ridge top is observed to be in the order of 1.6. The study indicates that there is PGA amplification on top of hills and de-amplifications occur at the base. Valleys and flat areas generally show low and negative amplification factors. It is observed that topographic features play significant roles in concentrating or diffusing seismic energy. This was confirmed by observing the behaviour of PGA values along selected profiles and by comparing PGA maps with and without topography. The study shows that topography has serious consequences and must be taken into account to minimise damage to infrastructures and safeguard human lives and suggests the importance of incorporating the effects of topographic features in any seismic hazard analysis schemes in densely populated mountainous regions. [ABSTRACT FROM AUTHOR]

The Unumherin community in Nigeria's Niger Delta is home to coastal marine polluted zones, and this research examines the radioactivity distributions and biohazard in the coastal environment. The activity concentrations of 40K, 238U, 232Th, as well as the outdoor dose rate of contaminated coastlines were measured using a calibrated RS-125 Gamma-Spec and a NaI(Tl) gamma-detector. The laboratory examination of sediments, water, and fish from the same coastal region -- Clarias gariepinus Pseudotropheus elongated Oreochromis niloticus and Stromateus fiatola -- was combined with the in-situ observations of gamma dose rates. With a value of 100nGy=h, the hotspot at site 4 is shown by the geographic distribution of gamma dose rates. The findings showed that the activities of the primordial radionuclides varied, with average values for the sediments and water exceeding suggested limits. Similarly, the corresponding mean hazard indices mostly exceeds the allowable limits. The species specificity of the fish species accounts for the variation in the mean concentrations of 40K, 238U and 232Th. C. gariepinus, having accumulated higher concentrations of 40K and 238U, may be the first to elicit health hazards in the future if pollution continues unmonitored. Hence, continuous monitoring of the aquatic environment alongside is highly recommended. [ABSTRACT FROM AUTHOR]

Amidst the rapid urbanisation, the ecological environment of plateau lake basins faces significant challenges. To better coordinate the relationship between urbanisation development and ecological environment construction, this paper conducts an analysis and assessment of the ecological resilience and urbanisation coupling coordination in Dali Prefecture based on sociogeographical spatial data. Initially, an assessment model for ecological resilience is established using resilience theory. Subsequently, the urbanisation development level is evaluated based on panel data. Finally, a coupling coordination model is employed to calculate the coupling coordination between the two aspects. The results indicate: (1) a rapid increase in basin urbanisation from 2010 to 2019, particularly between 2010 and 2015; (2) a continuous expansion of lowvalue ecological resilience areas across the entire basin, with a predominance of townships experiencing fluctuating decreases or stagnation, leading to an overall negative trend in ecological resilience indices; (3) evident spatial and temporal disparities in coupling coordination between the two systems in the basin's villages and towns, primarily reflected in differences in coupling coordination types, with a few towns consistently exhibiting environmental lag. This study introduces a customised ecological resilience model for the Erhai Basin, enhancing comprehension of urbanisation-ecological resilience dynamics in plateau lake basins. [ABSTRACT FROM AUTHOR]

Many indigenous cultures conceptualize health wholistically, whereby physical, mental, spiritual and relational dimensions of health are interconnected. Yet, quantitative approaches to studying Indigenous health remain anchored in western perspectives, that separate the dimensions of health. This paper aims to operationalize a wholistic indicator of health based on the IQI model of Inuit health. Variables from the 2017 Nunavik Health Survey (N = 1196) were selected based on their representativeness of IQI model. Exploratory Latent Class Analysis (LCA) was used to identify wholistic health profiles. Once participants assigned to their health profile, sociodemographic characteristics were compared across profiles, and multinomial regression models were used to examine the relationship between community-level social determinants of health and the profiles. The LCA revealed three health profiles, labelled as "excellent", "good" and "fair" based on the distribution of answers to the indicators. Nunavimmiut in "excellent" and "good" health were more likely to: rate their health positively; be over 30 years old; be in a relationship; and have participated or volunteered in community events. Nunavimmiut in "fair" health tended to report lower levels of community cohesion, family relationships, and emotional support. Intergrating culturally relevant models of health can support improved health status assessments and identify opportunities for health promotion. [ABSTRACT FROM AUTHOR]

Telerehabilitation is proposed as a promising avenue to enhance service accessibility for Indigenous communities, yet its application for Indigenous children remains relatively unexplored. This scoping review followed the PRISMA-ScR framework to explore current knowledge on the use of telerehabilitation for Indigenous children. Ten scholarly databases, seven grey literature databases, reference searches, and expert consultations were utilised to identify relevant studies. Included articles discussed the use of telerehabilitation provided by rehabilitation professionals (e.g. occupational therapist (OT), physical therapist (PT), speech and language pathologist (SLP) to Indigenous children and/or caregivers. Seven studies were included. Telerehabilitation was explored in different ways, the most common being real-time videoconferencing by SLPs. While some studies explicitly acknowledged cultural responsiveness within both the research process and the intervention, most were not designed for Indigenous children and their caregivers; rather, these participants were included with non-Indigenous participants. Successful implementation and sustainability of telerehabilitation services requires addressing technological limitations, understanding, and respecting diverse worldviews, and co-developing services to meet the unique needs of Indigenous families. Telerehabilitation has been rarely used with Indigenous children and when it was, little attention was given to cultural considerations. These findings emphasise that future telerehabilitation interventions should be truly community-led to ensure cultural relevance. [ABSTRACT FROM AUTHOR]

Inuit living in the northern region of Nunavik continue to experience significant health inequalities, which are rooted in colonialism that still have repercussions on their health-related perceptions and practices, including vaccination. This study aimed to explore the perceptions and determinants of routine vaccination among the Inuit of Nunavik by describing factors influencing vaccination decisions from the perspective of community members and health professionals. Semi-structured interviews focusing on the perception of vaccination and experience with vaccination and health services were conducted with 18 Inuit and 11 non-Inuit health professionals. Using the socio-ecological model, factors acting at the community and public policy (e.g. rumours and misinformation about vaccination, language barrier), organisational (e.g. complexity of the vaccination process, staff turnover, lack of specialised vaccination workers and interpreters), and intrapersonal and interpersonal (e.g. past experiences with vaccination, vaccine attitudes, social norms) levels were identified as having an impact on vaccination decisions. Improving vaccination coverage in Nunavik requires a more global reflection on how to improve and culturally adapt the health care and services offered to the Inuit population. [ABSTRACT FROM AUTHOR]

Globally, self-employed people were among the hardest hit by the repercussions of the COVID-19 pandemic and faced hardships such as financial decline, restrictions, and business closures. A plethora of financial support measures were rolled out worldwide to support them, but there is a lack of research looking at the effect of the policy measures on self-employed people. To understand how different governmental financial support measures enhanced the resilience of the self-employed and improved their ability to manage the pandemic, we conducted a mixed-method study using policy analysis and semi-structured interviews. The documents described policies addressing governmental financial support in Sweden and Canada during the pandemic, and the interviews were conducted with Swedish and Canadian self-employed people to explore how they experienced the support measures in relation to their resilience. The key results were that self-employed people in both countries who were unable to telework were less resilient during the pandemic due to financial problems, restrictions, and lockdowns. The interviews revealed that many self-employed people in hard-hit industries were dissatisfied with the support measures and found them to be unfairly distributed. In addition, the self-employed people experiencing difficulties running their businesses reported reduced well-being, negatively affecting their business survival. [ABSTRACT FROM AUTHOR]

Remote Arctic communities have often been depicted as being particularly vulnerable to the challenges of disasters, with their location and lack of infrastructure exacerbating risk. This study explores the characteristics of local resilience in the Arctic using the case study of the communities of the northwestern Westfjords. A total of 42 semi-structured interviews were carried out with various community members, seeking to uncover the features of inbuilt resilience that contribute to successes and vulnerabilities. These were transcribed, coded, and categorised in relation to an integrated framework for assessing community resilience in disaster management, which groups topics via the themes of environmental, social, governance, economic, and infrastructure. All themes played a role in the success of local coping strategies, with easy access to the natural environment central to physical and mental well-being. Despite this, vulnerabilities of the community were evident, including insufficient local healthcare workers during a severe COVID-19 outbreak in a care home, the absence of a local quarantine hotel, and insufficient information in foreign languages for non-natives of Iceland. The general trend of following rules and expert advice was demonstrative of strong social capital, with locals trusting those in charge, nationally and locally, to manage the pandemic. [ABSTRACT FROM AUTHOR]

Background: Retina fundus images conducted in Greenland are telemedically assessed for diabetic retinopathy by ophthalmological nurses in Denmark. Applying an AI grading solution, in a Greenlandic setting, could potentially improve the efficiency and cost-effectiveness of DR screening. Method: We developed an AI model using retina fundus photos, performed on persons registered with diabetes in Greenland and Denmark, using Optos® ultra wide-field scanning laser ophthalmoscope, graded according to ICDR. Using the ResNet50 network we compared the model's ability to distinguish between different images of ICDR severity levels in a confusion matrix. Results: Comparing images with ICDR level 0 to images of ICDR level 4 resulted in an accuracy of 0.9655, AUC of 0.9905, sensitivity and specificity of 96.6%. Comparing ICDR levels 0,1,2 with ICDR levels 3,4, we achieved a performance with an accuracy of 0.8077, an AUC of 0.8728, a sensitivity of 84.6% and a specificity of 78.8%. For the other comparisons, we achieved a modest performance. Conclusion: We developed an AI model using Greenlandic data, to automatically detect DR on Optos retina fundus images. The sensitivity and specificity were too low for our model to be applied directly in a clinical setting, thus optimising the model should be prioritised. [ABSTRACT FROM AUTHOR]