A Deep Learning Approach for the Detection and Prediction of Tuberculosis Using Chest X-Ray Imaging

Abstract

Tuberculosis (TB) is a major global health concern, particularly in resource-limited settings where diagnosis in the initial phase is crucial but limited by limited radiologists and diagnostic centers. This study develops an artificial intelligence-based model for early diagnosis and prediction of TB by chest radiography using a Convolutional Neural Network (CNN) and CNN-Long Short-Term Memory (CNN-LSTM) hybrid model for binary classification (TB-positive or TB-negative). A 10,000 chest X-ray image dataset, comprising 4,000 images from Ayder Comprehensive Specialized Hospital, Ethiopia, and 6,000 images from Kaggle, was preprocessed, augmented, and split into 80% for training and 20% for testing. Expert annotations ensured firm ground truth. The CNN model worked with 86% accuracy, with precision, recall, and F1-score of 0.86, while CNNLSTM achieved 85%, both running smoothly on quite modest hardware. The CNN functioned slightly better than the hybrid model, depicting superior discriminative capacity. The machine learning technique offers an inexpensive, scalable way to enhance early TB diagnosis and forecasting in high-burden, low-resource environments, reducing the diagnostic delay and supporting medical staff in nations like Ethiopia.