An explainable hybrid deep learning system for tuberculosis detection with Grad-CAM

Tuberculosis (TB) is a highly contagious disease that affects millions of individuals globally. Early detection is crucial in preventing its spread and improving patient outcomes. Radiologists often utilize X-ray imaging as a diagnostic tool for tuberculosis; however, the accuracy of the results may differ depending on the radiologist’s interpretation. To increase the precision of TB identification from X-ray images, a hybrid strategy combining Convolutional Neural Network, Histogram of Oriented Gradients, and Quantum Support Vector Machine (QSVM) classifier has been developed. Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning. By combining the strengths of both techniques, this approach aims to capture relevant features and shape and texture information of an image. The study used state-of-the-art models such as VGG16 and AlexNet, along with a handcrafted method (HOG) and a deep learning (CNN) method to detect TB from chest X-ray images. Six distinct tests were carried out by the authors to successfully diagnose tuberculosis. All models showed promise when the data was analyzed, but the best techniques involved augmentation, image preprocessing, contrast improvement, and noise filtering. The block-matching and 3D filtering approach was used to improve image edge preservation and reduce noise. In this study, Grad-CAM (gradient-weighted class activation map) was applied to the convolutional neural network model to identify the model’s important features (explainability) for decision-making. This focus on Explainable AI (XAI) is crucial for clinical adoption, as it provides radiologists with visual evidence of the model’s reasoning, fostering trust and enabling more informed decisions. The hybrid model was found to have achieved an exceptional level of accuracy in detecting and classifying TB, with an accuracy rate of 100% during training, 99.07% during validation, and 98.14% during testing. These results highlight the effectiveness of the hybrid model in accurately identifying TB and its potential to be a valuable tool in the fight against this disease.