A HYBRID MODEL FOR CLASSIFICATION OF TUBERCULOSIS CHEST X-RAYS IMAGES

Abstract

Tuberculosis (TB), a grave infectious disease affecting millions globally, is often diagnosed using chest X-rays. For accurate diagnosis, especially for detecting early stage, medical practitioners require the assistance of advanced technologies. In contrast to existing models, which focus largely on TB detection in the images, the proposed work aims to classify the images affecting TB such that an appropriate method can be chosen for accurate chest TB detection in chest X-ray images. Thus, we aim to combine the powerful features of the VGG16 architecture with a convolutional neural network (CNN) for classification purposes. Drawing inspiration from VGG16, known for its effective method of capturing essential image information, we aim to modify VGG16 for feature extraction to identify signs of tuberculosis (TB) in images. For the classification task, we employ a CNN to categorize images impacted by TB. Our proposed technique is evaluated on a standard dataset, demonstrating its superiority over current leading methods in accuracy, recall, and precision.