WAVELET BASED CNN FOR DIAGNOSIS OF LUNG DISEASES USING CHEST X-RAYS

Abstract

Lung diseases are widespread worldwide, including conditions such as lung nodules, pneu monia, asthma, tuberculosis, fibrosis, etc. It is crucial to diagnose lung disease promptly. In this study, wavelet-based Convolutional Neural Networks have been employed to detect and differentiate various types of lung diseases through analyzing chest X-ray images. Existing CNN architectures have been used previously to classify healthy and affected condition chest X-rays. However, these networks process the image in a single resolution and may lose poten tial features present in other resolutions of the input image. Over this normal CNN, Wavelets are utilized to decompose the image into different spatial resolutions based on high pass and low pass frequency components and extract valuable features from the affected portion of lung X-ray images efficiently. A CNN model of wavelet is employed to find relevant features from the X-ray images, and SVM classifier is incorporated to classify different lung diseases from the extracted features. The proposed framework is tested on three publicly available datasets and the method achieved an average accuracy of 100% using the first dataset (NIH Chest X-ray), 100% accuracy on the second dataset (LUNG DISEASE), and 96% accuracy on the third dataset (JSRT). Overall, the proposed approach outperformed existing works and demonstrated its effectiveness in identifying multiple lung conditions, including nodules, COVID-19, and other ailments, making it a versatile tool