A Good Evaluation Based on Confusion Matrix for Lung Diseases Classification using Convolutional Neural Networks

Izza Putri Kamila; Christy Atika Sari; Eko Hari Rachmawanto; Nur Ryan Dwi Cahyo

doi:10.26877/asset.v6i1.17330

A Good Evaluation Based on Confusion Matrix for Lung Diseases Classification using Convolutional Neural Networks

Izza Putri Kamila, Christy Atika Sari, Eko Hari Rachmawanto, Nur Ryan Dwi Cahyo

Abstract

CNN has been widely used to detect a pattern with image classification. This study used CNN to perform a classification analysis of lung abnormality detection on chest X-ray images. The dataset consists of 5,732 2D images with dimensions of 200 x 200 x 1 divided into training data (85%) and testing data (15%). The preprocessing process includes image resizing, enhancement to increase contrast and reduce image complexity, and filtering to improve visibility and reduce noise. CNN is used to classify imagery into three categories, Normal (no abnormalities), Pneumonia, and Tuberculosis. The results showed a good level of accuracy, with an average accuracy of 97.24% in 3 trainings, and a 100% success rate in 6 classification experiments. This research provides insights into the detection of lung disorders and encourages further exploration in medical diagnosis.

Keywords

Pneumonia; Tuberculosis; TBC; CNN; Image Classification

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DOI: https://doi.org/10.26877/asset.v6i1.17330

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