MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization

Author:

Deepak Gerard1ORCID,Madiajagan M.2,Kulkarni Sanjeev3,Ahmed Ahmed Najat4,Gopatoti Anandbabu5,Ammisetty Veeraswamy6

Affiliation:

1. Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

3. Department of Information Science and Engineering, Yenepoya Institute of Technology, Mangalore, Karnataka, India

4. Department of Computer Engineering, Lebanese French University, Erbil, Iraq

5. Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India

6. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

Abstract

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.

Publisher

IOS Press

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation

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1. Integrating Decision Theory and Syntactic Data for Enhanced Rough Fuzzy C-Means Clustering Algorithm;2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS);2023-12-11

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