{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:48:57Z","timestamp":1777891737640,"version":"3.51.4"},"reference-count":50,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["XST"],"published-print":{"date-parts":[[2023,5,11]]},"abstract":"<jats:p>BACKGROUND: COVID-19 is the most dangerous virus, and its accurate diagnosis saves lives and slows its spread. However, COVID-19 diagnosis takes time and requires trained professionals. Therefore, developing a deep learning (DL) model on low-radiated imaging modalities like chest X-rays (CXRs) is needed. OBJECTIVE: The existing DL models failed to diagnose COVID-19 and other lung diseases accurately. This study implements a multi-class CXR segmentation and classification network (MCSC-Net) to detect COVID-19 using CXR images. METHODS: Initially, a hybrid median bilateral filter (HMBF) is applied to CXR images to reduce image noise and enhance the COVID-19 infected regions. Then, a skip connection-based residual network-50 (SC-ResNet50) is used to segment (localize) COVID-19 regions. The features from CXRs are further extracted using a robust feature neural network (RFNN). Since the initial features contain joint COVID-19, normal, pneumonia bacterial, and viral properties, the conventional methods fail to separate the class of each disease-based feature. To extract the distinct features of each class, RFNN includes a disease-specific feature separate attention mechanism (DSFSAM). Furthermore, the hunting nature of the Hybrid whale optimization algorithm (HWOA) is used to select the best features in each class. Finally, the deep-Q-neural network (DQNN) classifies CXRs into multiple disease classes. RESULTS: The proposed MCSC-Net shows the enhanced accuracy of 99.09% for 2-class, 99.16% for 3-class, and 99.25% for 4-class classification of CXR images compared to other state-of-art approaches. CONCLUSION: The proposed MCSC-Net enables to conduct multi-class segmentation and classification tasks applying to CXR images with high accuracy. Thus, together with gold-standard clinical and laboratory tests, this new method is promising to be used in future clinical practice to evaluate patients.<\/jats:p>","DOI":"10.3233\/xst-221360","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T11:29:49Z","timestamp":1677583789000},"page":"483-509","source":"Crossref","is-referenced-by-count":4,"title":["MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization"],"prefix":"10.1177","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0466-2143","authenticated-orcid":false,"given":"Gerard","family":"Deepak","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Madiajagan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjeev","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"Department of Information Science and Engineering, Yenepoya Institute of Technology, Mangalore, Karnataka, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Najat","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Lebanese French University, Erbil, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anandbabu","family":"Gopatoti","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, 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