Enhancing Respiratory Disease Diagnosis through Deep Learning: A CNN-Based Approach for Image Classification

Early and accurate identification is crucial for the efficient treatment of respiratory diseases. Leveraging the capabilities of Deep-Learning (DL), this work presents a Convolutional Neural Network (CNN) model for the detection of respiratory diseases utilizing chest X-ray images. We begin by discussing the significant impact of respiratory diseases on human health and the critical need for timely diagnosis. Our proposed CNN model is meticulously designed to classify these X-ray images, distinguishing between normal lung conditions and pneumonia cases. The results showcase an accuracy of 93.62% without normalization and 95.03% with normalization in this crucial diagnostic task. Furthermore, this work lays the groundwork for future enhancements, including image enhancement techniques, advanced feature extraction, and classification models, with the overarching goal of advancing respiratory disease diagnosis and patient care. Through this research, we contribute to the ongoing efforts to combat respiratory diseases, empowering healthcare practitioners with efficient and accurate diagnostic tools.