Classification of Lung Diseases using Deep Learning Techniques: A Comparative Study of Classification Algorithms

Abstract

The significant health impact of lung diseases hampers the life of an individual and his/her family. It is crucial to ensure that everyone lives a healthy life, hence early detection of lung diseases is encouraged at an early stage. As several lung illnesses reduce the life span of people, they are not able to live a healthy life. There are errors in many detection algorithms, so a better algorithm is required to detect such diseases. In this paper, we have discussed lung diseases and how to recognize them. The two primary techniques for identifying lung illness are therefore image processing and deep learning. Deep learning is increasingly emphasized as the preferable method with convolutional neural networks. We further discussed various machine learning algorithms and compared their results with the newly designed algorithm of a convolutional neural network with an autoencoder. There are several approaches described in the literature for classifying medical images. This paper aims to develop a useful tool that will assist medical practitioners in quickly determining if a patient has a lung disease or is at risk of contracting one; by analyzing lung images and examining disease development risk factors with the use of an autoencoder.