Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Abstract

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

Method for detecting defects in direct metal deposition using a neural network

“…Based on this, AI algorithms can predict the likelihood of developing certain conditions or complications. Also, authors of paper "Neural Networks for the Detection of COVID-19 and Other Diseases" [3] proposed a novel deep learning model called ConXNet, which has been trained and tested using different datasets to improve the accuracy of COVID-19 detection by up to 98%. This contribution is significant as it offers an innovative approach to enhance the detection capabilities of AI-based models in the context of a global health crisis.…”

Enhancing the classification accuracy by intra-concentration and the distance between the class boundaries instead of the class centers

“…Convolutional Neural Networks (CNNs) have gained significant attention in the field of machine learning due to their remarkable performance on computer vision tasks [1]. CNNs have achieved state-of-the-art results on a diverse range of image classification and recognition problems, including object detection [2,3], face recognition [4][5][6][7], segmentation [8,9] and scene classification [10,11] and Their success has led to widespread adoption in various industries, including healthcare [12,13], self-driving cars [14], and more. During the training of deep neural network models, a loss function is defined to guide the adjustment of parameters during backpropagation process.…”

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

“…In the field of image-based melanoma diagnosis, the development of deep learning and, in particular, convolutional neural networks (CNNs) has reduced reliance on manual-feature extraction techniques. CNN-based classification methods have also demonstrated diagnostic effectiveness comparable to that of dermatologists [ 19 ]. In [ 20 ], researchers mainly concentrated on the automatic identification and categorization of skin cancer, as computer-aided screening technologies had become more prevalent.…”