Classification of Lung Diseases Using Machine Learning Technique

Abstract

The advancement of medicine depends on the use of modern technologies. To provide precise and tailored treatment options for a variety of illnesses, comprehensive research conducted in collaboration with medical experts, patients, and researchers is crucial. This study uses deep learning to determine the acceptable level of accuracy in the medical field based on data that is accessible to the public. To begin, we took the annotated lung sound recordings and extracted the spectrogram features and labels to feed into our 2D Convolutional Neural Network (CNN) model. In this work, we address the issue of scarce medical data by employing small-volume datasets with less than a thousand samples to identify pulmonary disorders from chest X-ray images. Deep learning algorithms are utilized to treat pneumonia, tuberculosis, lung cancer, and other lung diseases. A review of various typical deep-learning network topologies used in medical image processing is also provided. Our ensemble model achieved a classification accuracy of 96.2% and an F1-score of 96.1 %, outperforming individual models such as VDSNet, ResNet18, DenseNet201, and SqueezeN et. This demonstrates the effectiveness of our approach in enhancing diagnostic accuracy for lung disease classification