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

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

Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 6, No 1 (2024): November-January

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.