Machine learning based Lung Disease Prediction Using Convolutional Neural Network Algorithm

Abstract

Lung disease prediction is a critical issue in today's world. However, in the past two years, the corona virus disease 2019 (COVID-19) has a broad range and, in a limited percentage of people, a notable effect on the lungs. In the past, the fuzzy logic method has been used to classify lung disease prediction, but it has faced challenges such as difficulties in identifying segmented regions and output inaccuracies. To solve the issue Convolutional neural networks are used in machine learning to predict lung condition. The preprocessing based weighted average filter gives greater weight to the central value, making its contribution more significant than that of other values and can regulate the degree of image blurring. The process of segmenting based on region split and merge techniques involves separating one or more areas or entities in an image according to a size of m by n at one level of a threshold value. This segmented in multiple sub-regions of the same size, indicating a fundamental representational structure, from that Image classification using convolutional neural networks (CNNs) is a type of neural network specifically designed to extract distinct characteristics from segmented data. They are often used in tasks such as lung disease prediction and recognition due to their ability to identify intricate details in clustered data. The approach was evaluated using the MATLAB tool, a novel CNN with multiple image processing technique in our experiment to efficiently classify lung illnesses under typical circumstances, the average accuracy increased up to 97%. The results of this study show significant improvement in the prognosis of lung prediction in medical filed.