A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Abstract

A pneumonia of unknown causes, which was detected in Wuhan, China, and spread rapidly throughout the world, was declared as Coronavirus disease 2019 (COVID-19). Thousands of people have lost their lives to this disease. Its negative effects on public health are ongoing. In this study, an intelligence computer-aided model that can automatically detect positive COVID-19 cases is proposed to support daily clinical applications. The proposed model is based on the convolution neural network (CNN) architecture and can automatically reveal discriminative features on chest X-ray images through its convolution with rich filter families, abstraction, and weight-sharing characteristics. Contrary to the generally used transfer learning approach, the proposed deep CNN model was trained from scratch. Instead of the pre-trained CNNs, a novel serial network consisting of five convolution layers was designed. This CNN model was utilized as a deep feature extractor. The extracted deep discriminative features were used to feed the machine learning algorithms, which were k-nearest neighbor, support vector machine (SVM), and decision tree. The hyperparameters of the machine learning models were optimized using the Bayesian optimization algorithm. The experiments were conducted on a public COVID-19 radiology database. The database was divided into two parts as training and test sets with 70% and 30% rates, respectively. As a result, the most efficient results were ensured by the SVM classifier with an accuracy of 98.97%, a sensitivity of 89.39%, a specificity of 99.75%, and an F-score of 96.72%. Consequently, a cheap, fast, and reliable intelligence tool has been provided for COVID-19 infection detection. The developed model can be used to assist field specialists, physicians, and radiologists in the decision-making process. Thanks to the proposed tool, the misdiagnosis rates can be reduced, and the proposed model can be used as a retrospective evaluation tool to validate positive COVID-19 infection cases.

Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

“…Eventually, the researchers achieved 96.70% accuracy and 92.7% f-1 score. Noor et al [23] Used deep learning networks to automatically detect 3 classes of pneumonia (viral, COVID-19, and, normal) based on chest X-ray images. Their proposed model for the feature extraction section consisted of 5 convolutional layers.…”

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

“…Nour et al [ 12 ] developed a new clinical diagnosing approach of COVID-19 for adopting medical functions. The method has relied on deep features as well as Bayesian optimization.…”

Real-time measurement of the uncertain epidemiological appearances of COVID-19 infections

“…As a result, they find that one of the kits tested was fully compatible with direct SARS-Cov-2 disease detection and diagnosis. Due to this difficulty to carry out the required tests to control the pandemic via the above mentioned kits, different researchers have proposed alternative methods to support diagnosis, for example, those based on deep learning techniques that process X-ray images [17] , [18] , [19] .…”