Detection of tuberculosis using customized MobileNet and transfer learning from chest X-ray image

Abstract

One of the most contagious diseases in the world, tuberculosis (TB) is brought on by the bacteria Mycobacterium tuberculosis. This hazardous scenario can cause life losses and requires expert doctors and several hours to detect the disease. Using the MobileNet transfer learning model, a computationally lightweight model has been proposed in this study. The optimal model for the diagnosis of tuberculosis has been determined after testing numerous variants on the base model with pre-trained weights. A computationally light transfer learning model is proposed to obtain the maximum overall accuracy of 98.66%. The improvement over the best existing model is quite significant. The transfer learning model (DenseNet) utilized in this existing model is based on a very complex convolutional neural network (CNN), and as a result, the model requires greater amounts of time. The performance of the other existing models is relatively less in comparison to our proposed model, and the methods have a number of other drawbacks. Our goal in this work is to create a more accurate model that requires less computational effort. When compared to previous models, our model has a very less number of trainable parameters, which causes the model to converge more quickly and predict more accurately. Our approach also has the benefit of being simply able to modify its weights when the system is further updated with new datasets. Additionally, because of its lightweight architecture, it can be installed on mobile devices as well as used in web-based applications with ease. To analyze and validate the proposed method, we have collected data from Kaggle and used MC, CHN and NIH datasets.