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The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorith...
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... engineering [35] and construction [36,37]. For signal and image processing, such methods are commonly used to classify objects and perform ROI detection and segmentation. A CNN consisting of four layersthe input, convolution, polling, and connected layers-was used to extract features from the COVID-19 images. The structure of the CNN is shown in Fig. 6. The operation of the CNN was as ...
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COVID-19 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying COVID-19 from other types of pneumonia and normal cases provides clinicians with tremendous conveniences in diagnosis process. In this paper, a deep ensemble dynamic learning network (DEDLN) is proposed. A...
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... By forecasting patient volumes and resource needs, healthcare providers can better manage staffing levels, bed utilization, and supply chains. During the COVID-19 pandemic, predictive models were crucial in predicting the demand for ICU beds, ventilators, and personal protective equipment, allowing hospitals to allocate resources more effectively and avoid shortages by Alsaade in 2021 [41]. ...
The integration of advanced data analytics and predictive modeling in healthcare is revolutionizing
decision-making processes, improving patient outcomes, and optimizing resource allocation. This paper explores the
application of these technologies in healthcare management, focusing on their ability to analyze vast datasets,
uncover patterns, and predict future outcomes. We evaluated various predictive models, including Logistic
Regression, Decision Trees, Random Forests, Gradient Boosting Machines (GBM), and Neural Networks, using a
comprehensive dataset from electronic health records (EHRs). Our findings indicate that Neural Networks and
GBM outperformed other models in terms of accuracy, precision, recall, F1-score, and AUC-ROC, demonstrating
their robustness in handling complex healthcare data. The study also highlights the importance of data quality,
model interpretability, and ethical considerations in the deployment of predictive models. By leveraging these
advanced analytical tools, healthcare providers can enhance clinical decision-making, personalize treatment plans,
and allocate resources more efficiently. This paper contributes to the ongoing efforts to harness the power of
predictive analytics in healthcare, emphasizing the need for further research to overcome existing challenges and
fully realize the potential of these technologies.
... Researchers at the University of Missouri discovered that autistic children differ from typically developing children in one key facial feature [61]. There is also a shorter midface and a bigger upper face (those facial features consist of cheeks, noses, as well as wide-set eyes) than the lower face. ...
Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to more crises and mental convulsions. In order to teach students with ASD, it is essential to understand the impact of their learning environment on their interaction and behavior. Different methods have been used to diagnose ASD in the past, each with their own strengths and weaknesses. Research into ASD diagnostics has largely focused on machine learning algorithms and strategies rather than diagnostic methods. This article discusses many diagnostic techniques used in the ASD literature, such as neuroimaging, speech recordings, facial features, and EEG signals. This has led us to conclude that in schools and educational settings, autism can be diagnosed cheaply, quickly, and accurately through face analysis. To facilitate and speed up the processing of facial information among children in educational settings, we applied the AlexNet architecture designed for edge computing. A fast method for detecting autism spectrum disorders from the face can be applied to educational settings using this structure. While we have investigated a variety of methods, the face can provide us with appropriate information about the disorder. In addition, it can produce more interpretive features. In order to help students in schools who are suffering from this disease, key factors must be considered: potential clinical and therapeutic situations, efficiency, predictability, privacy protection, accuracy, cost-effectiveness, and lack of methodological intervention. The diseases are troublesome, so they should be identified and treated.
... It is now possible to use deep l9earning approaches to solve problems that traditional ANNs have trouble with [4][5][6][7]. ANNs have been used for a broad variety of tasks, including but not limited to the following: the classification of written texts, the construction of image processing-based recognition systems, and the analysis of enormous volumes of scientific data [8,9]. ...
Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.
... According to a paper titled Role of facial expressions in social interactions [26], the human face conveys the most important information in linguistic communication and social interaction. Researchers at the University of Missouri have found that autistic children have similar facial features to those without autism [6]. On top of this, the studyfrom [7] uses the CNN algorithm to train data for extracting components of human facial expressions. ...
Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interaction, communication, and behavior. Early intervention plays a pivotal role in enhancing cognitive abilities and reducing autistic symptoms in children with ASD. Numerous clinical studies have highlighted distinctive facial characteristics that distinguish ASD children from typically developing (TD) children. In this study, we propose a practical solution for ASD screening using facial images using YoloV8 model. By employing YoloV8, a deep learning technique, on a dataset of Kaggle, we achieved exceptional results. Our model achieved a remarkable 89.64% accuracy in classification and an F1-score of 0.89. Our findings provide support for the clinical observations regarding facial feature discrepancies between children with ASD. The high F1-score obtained demonstrates the potential of deep learning models in screening children with ASD. We conclude that the newest version of YoloV8 which is usually used for object detection can be used for classification problem of Austistic and Non-autistic images.
... Segmentation, localization, pattern classification, and extraction of Regions of Interest (RoIs) of chest X-rays or CT images play a particular role in Image classification (Kaheel et al., 2021). Outstanding results from different countries show that using image processing to analyze lung X-ray images, COVID-19 cases could be identified among pneumonia and healthy controls (Irmak, 2020;Alsaade et al., 2021;Civit-Masot et al., 2020;Fontanellaz et al., 2021;. Yang et al. (2021) designed a framework to find out the best architecture, pre-processing and training parameters by pre-trained Convolutional Neural Network (CNN) models and using deep learning techniques for the COVID-19 CT-scan classification tasks. ...
... Deep learning algorithms are currently being used to address issues that are difficult to solve with regular ANNs [4][5][6][7]. ANNs have been used to analyze vast quantities of scientific data, learn from complicated information [8][9][10], evolve recognition systems utilizing image processing with analysis and classification of texts [11][12][13][14][15] among other things. ...
Melanoma is an abnormal proliferation of skin cells that arises and develops in most of the cases on surface of skin that is exposed to copious amounts of sunlight. This common type of cancer may develop in areas of the skin that are not exposed to a much abundant sunlight. The research addresses the problem of Segmentation and Classification of Melanoma Skin Cancer. Melanoma is the fifth most common skin cancer lesion. Bio-medical Imaging and Analysis has become more promising, interesting, and beneficial in recent years to address the eventual problems of Melanoma Skin Cancerous Tissues that may develop on Skin Surfaces. The evolved research finds that Attributes Selected for Classification with Color Layout Filter model. The research has produced an optimal result in terms of certain performance metrics accuracy, precision, recall, PRC (what is PRC? Expansion is needed in Abstract), The proposed method has yielded 90.96% of accuracy and 91% percent of precise and 0.91 of recall out of 1.0, 0.95 of ROC AUC, 0.87 of Kappa Statistic, 0.91 of F-Measure. It has been noticed a lowest error with reference to proposed method on certain dataset. Finally, this research recommends that the Attribute Selected Classifier by implementing one of the image enhancement techniques like Color Layout Filter is showing an efficient outcome.
... Thus, smoothing and sharpening can be considered image enhancement techniques (Hall et al. 1971). Generally, three types of filters such as guassian filter (Ambita et al. 2020;Alsaade et al. 2021;Vijaya and Suhasini 2014), median filter (Makaju et al. 2018), and wiener filter (Antony et al. 2021) can be used in the preprocessing of chest radiographs. The median filter is used for noise removal and smoothing of an image. ...
Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated. A tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations.
... Their model is also a capable for handling noisy data. Alsaade et al. [32] applied to eliminate noise from the chest X-ray radiograph images, and then the adaptive region growing technique was used to segment the region of interest from the chest X-ray radiographs. After segmentation, a hybrid feature extraction composed of 2D-DWT and gray level co-occurrence matrix was utilized to extract the features significant for detecting COVID-19. ...
... Compared to ANN (artificial neural networks), DNN (deep neural networks) performed well due to their multiple hidden layer features, which increased accuracy. However, because of the problem of vanishing gradients in DNNs, convolution neural networks (CNNs) were considered as an alternative [176][177][178][179]. Compared to other image recognition learning models, CNNs are proven to produce better outcomes. ...
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
... However, this layer is used to saturate or limit the output. The non-linearity layer is permanently embedded in the convolution layer (Aldhyani et al. 2020b;Alsaade et al. 2021). In the last two decades, two activation functions, sigmoid and tanh, have been mostly applied in deep learning techniques. ...
Alzheimer's disease (AD) is a high-risk and atrophic neurological illness that slowly and gradually destroys brain cells (i.e. neurons). As the most common type of amentia, AD affects 60–65% of all people with amentia and poses major health dangers to middle-aged and elderly people. For classification of AD in the early stage, classification systems and computer-aided diagnostic techniques have been developed. Previously, machine learning approaches were applied to develop diagnostic systems by extracting features from neural images. Currently, deep learning approaches have been used in many real-time medical imaging applications. In this study, two deep neural network techniques, AlexNet and Restnet50, were applied for the classification and recognition of AD. The data used in this study to evaluate and test the proposed model included those from brain magnetic resonance imaging (MRI) images collected from the Kaggle website. A convolutional neural network (CNN) algorithm was applied to classify AD efficiently. CNNs were pre-trained using AlexNet and Restnet50 transfer learning models. The results of this experimentation showed that the proposed method is superior to the existing systems in terms of detection accuracy. The AlexNet model achieved outstanding performance based on five evaluation metrics (accuracy, F1 score, precision, sensitivity, and specificity) for the brain MRI datasets. AlexNet displayed an accuracy of 94.53%, specificity of 98.21%, F1 score of 94.12%, and sensitivity of 100%, outperforming Restnet50. The proposed method can help improve CAD methods for AD in medical investigations.
