Tuberculosis Detection Using Convolutional Neural Network

Tuberculosis (TB) is a chronic lung disease caused by bacterial infection and remains one of the leading causes of mortality worldwide. Early and accurate detection of TB is crucial for effective treatment and prevention of complications. In this study, we propose a deep learning-based system for TB detection using chest X-ray images. The system leverages the VGG16 architecture for classification, supported by image preprocessing and data augmentation to improve model performance. The dataset comprises X-ray images categorized into TB-infected and normal cases. The VGG16 model was trained, validated, and tested to classify chest X-rays, achieving a train accuracy of 98.55%, validation accuracy of 99.75% and test accuracy of 98.73%, for TB detection. Our results demonstrate the effectiveness of the VGG16-based model in reliably detecting TB from chest X-rays while providing practical healthcare accessibility through the user interface. This combined approach can serve as a valuable tool in computer-aided TB diagnosis, enabling timely and accurate detection to improve patient outcomes. Key Words: Chest X-ray Classification, Deep Learning, Medical Image Analysis, Computer-Aided Diagnosis (CAD), Transfer Learning, TB Diagnosis.