Optimized tuberculosis classification system for chest X-ray images: Fusing hyperparameter tuning with transfer learning approaches

Abstract

Advanced diagnostic methods are necessary for the prompt and reliable identification of tuberculosis (TB), which continues to be a worldwide health problem. Globally, there were projected to be 10 million new cases of tuberculosis in 2021, of which 9.8 million affected adults and 0.2 million children. About 15% of fatalities worldwide are attributable to tuberculosis (1.5 million deaths for every 10 million infections). To create a reliable model for tuberculosis (TB) identification using chest X-ray pictures, we use deep learning approaches in this work, namely Convolutional Neural Networks (CNNs) and a combination of transfer learning and hyperparameter tuning. The dataset provides a varied selection of 3500 normal and 700 TB-infected patients. It consists of 4200 photos that were obtained from the “Tuberculosis (TB) Chest X-ray Database” on Kaggle. By utilizing the benefits of a trained model, the suggested methodological approach incorporates transfer learning. To maximize the performance of the suggested model, hyperparameter adjustment is also used. Using the VGG19 pre-trained neural network, the model design is based on the concepts of transfer learning. The architecture makes use of task-specific layers, regularization methods, and deliberate layer freezing to enable sophisticated categorization. Training and assessment stages demonstrate encouraging outcomes, with an accuracy of almost 98% attained on a different test dataset. A more thorough examination highlights the need for caution when interpreting high accuracy, nevertheless, by highlighting possible difficulties.