Efficient Classification of Pulmonary Pneumonia and Tuberculosis Alongside Normal and Non-X-ray Images with Minimal Resources and Maximum Accuracy

1.1)Pneumonia, primarily caused by Streptococcus pneumoniae, and tuberculosis (TB), caused by Mycobacterium tuberculosis, continue to present significant global health challenges. Pneumonia is responsible for 14% of deaths among children under five, resulting in 740,180 fatalities annually [1]. Similarly, TB caused 1.25 million deaths in 2022, including 161,000 among individuals with HIV [2]. Misdiagnosis is a critical issue, with 22.3% of pneumonia cases being misidentified as TB [3], highlighting the need for accurate diagnostic tools. This study proposes a novel classification framework for chest X-ray (CXR) images, designed to identify four categories: normal, pneumonia, tuberculosis, and non-X-ray. By incorporating the "non-X-ray" class, the model enhances robustness by detecting outliers and unseen anomalies. The framework utilizes a pre-trained ResNet-18 convolutional neural network and a fine-tuned DenseNet-121, both trained with and without weighted loss function. The best-performing model achieved exceptional results, with 98.76% accuracy, 99.01% precision, and 99.03% recall, maintaining or surpassing class-wise performance. The model was trained on a curated dataset from multiple valid sources, containing 5,489 normal, 4,273 pneumonia, 4,197 tuberculosis, and 1,357 non-X-ray images. This framework has the potential to reduce misdiagnosis and improve healthcare delivery, particularly in resource-limited environments.