Pneumonia and tuberculosis detection and classification using deep learning in resource limited settings, Ethiopia

Abstract

The rising incidence of pneumonia and tuberculosis presents major public health challenges, particularly in resource-limited regions such as Ethiopia, where shortages of skilled radiologists hinder timely diagnosis. Although deep learning has shown promise in medical image analysis, robust multiclass classification of these diseases from chest X-ray images remains technically challenging. This study proposes a deep learning–based multiclass classification framework for automated detection of pneumonia and tuberculosis from chest X-rays. A total of 2,100 chest X-ray images were collected from major hospitals in the Amhara region. Images were preprocessed and augmented to improve robustness. Multiple convolutional neural network architectures including Sequential CNN, VGG19, MobileNetV3-Large, EfficientNetV2-B0/B1, and InceptionResNet were trained and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. A two-stage transfer learning strategy with systematic hyperparameter optimization was applied. Among the evaluated models, the customized VGG19 achieved the best performance, with 99% accuracy, precision, recall, and F1-score, and an AUC of 99.93%. Other models also demonstrated strong performance, with accuracies ranging from 95 to 98%. These results highlight the importance of model selection and optimization in medical image classification. An independent external validation using 3,568 chest X-ray images demonstrated robust generalization, with an accuracy of 88.83% and macro F1-score of 83.92%. Future work will focus on prospective multicenter validation, patient-level evaluation, and expert radiologist verification of the explainability framework.