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.