REVOLUTIONIZING LUNG DISEASE DIAGNOSIS IN X-RAY IMAGES: MULTI-STAGE APPROACH WITH WMF, FCM, AND MCNN INTEGRATION USING YOLOV4

Asst Professor, Dept of CSE, Sardar Patel Institute of Technology, Mumbai

Email Id: routray.sanju@gmail.com

This research presents a comprehensive framework for enhancing the accuracy of lung disease diagnosis through a multi-stage process applied to X-ray images. The proposed methodology begins with the application of Weighted Median Filtering (WMF) to effectively reduce noise in the input X-ray images. Subsequently, morphological operations are employed for enhancement, refining the image quality for better analysis. The segmentation process is then performed utilizing Fuzzy C-Means Clustering (FCM), effectively partitioning the images to highlight significant regions. To extract pertinent features for classification, the framework employs a Modified Convolution Neural Network (MCNN) within an Ensemble-based classification approach. The entire process is seamlessly integrated using YOLOv4, facilitating efficient end-to-end processing. Notably, the proposed methodology achieves a commendable accuracy rate of 93.78%, demonstrating its efficacy in robust lung disease diagnosis. This integrated framework offers a promising approach to enhance the accuracy and reliability of computer-aided diagnostic systems for lung diseases based on X-ray images.