Tuberculosis Detection based on Lung X-Ray Images Using Convolutional Neural Networks (CNN)

Abstract

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis, primarily affecting the lungs. Despite being preventable and curable, TB remains a significant global health issue, especially in developing countries. The success of TB treatment heavily depends on the accuracy of the diagnosis, which typically requires expertise from pulmonology or radiology specialists to interpret chest X-ray images.  This study aims to design an assistive tool for TB detection that can automatically diagnose the disease using chest X-ray data.  The study implemented a Convolutional Neural Network (CNN) architecture to analyze the X-ray images. Additionally, image preprocessing and early stopping methods were employed to enhance accuracy performance, optimize computation, and prevent overfitting.  Experiment was conducting using 75% of the data as training data to generate the model and then applied to 25% of the data as testing data. This study comparing image sizes in RGB and grayscale modes. Experimental results show that the use of early stopping has a significant impact on training time, reducing training time substantially in almost all scenarios without drastically sacrificing accuracy. Without early stopping, accuracy does tend to be higher, as seen in grayscale color mode with an image size of 128x128, where the accuracy reaches 0.992, and in RGB mode with an image size of 64x64 which reaches 0.995. However, training time also increases significantly, for example for a 299x299 image with RGB mode, the training time reaches 927 seconds. Therefore, while RGB yields slightly higher accuracy, grayscale is recommended due to significantly faster training times. Additionally, the early stopping mechanism proves effective in reducing computational time, making the training process more efficient.