Tuberculosis Detection from Chest X-Ray Images Using CNNs and Ant Colony Optimization

Tuberculosis (TB) remains a leading global health challenge, especially in resource-constrained regions due to limited access to expert radiology. To address this diagnostic gap, this study introduces a deep learning-based framework for automated TB detection using chest X-rays. The proposed model makes use of Convolutional Neural Networks (CNNs) to find and sort features that are important for the detection of TB wherein Ant Colony Optimization (ACO) is used as a metaheuristic method to improve hyperparameters. This approach enables the system to overcome local minima, a common limitation in traditional gradient-based algorithms by simulating the foraging behavior of ants, the ACO efficiently explores the search space for optimal learning rate, batch size, and network architecture. The pipeline achieves an accuracy of around 98% through extensive preprocessing steps-including adaptive histogram equalization, normalization, and data augmentation, CNN training, and careful evaluation to improve model generalization and address class imbalance on publicly available TB chest X-ray datasets from Kaggle, significantly outperforming traditional optimization methods. Local Interpretable Model-agnostic Explanations (LIME) are used to ensure transparency and build trust by letting the doctors view the most important areas that affect TB predictions, which boosts their confidence in the diagnosis. This research shows that scalable, explainable AI-powered TB screening is possible.