Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images
Source abstract
Abstract
A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · AI in cancer detection · Phonocardiography and Auscultation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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