Development of Light-Weight Convolutional Neural Network Model to Diagnose Tuberculosis

Abstract

Tuberculosis (TB) is a contagious chest infection. World Health Organization has introduced different TB control programs in various countries.  For the diagnosis of TB, the doctors mostly recommend chest X-ray (CXR) because it is more cost-effective and less time-consuming than existing sputum tests and Tuberculosis Skin Tests (TST). As per the research, deep learning models are best for TB diagnosis, by using CXR rather than normal eye-sight-based traditional method. Since doctor’s eye-sight or his experience is prone to human error, therefore, to solve this problem many Convolutional Neural Network (CNN) based models are introduced. Some of these models have high computational costs, and better accuracy making them heavy model. Whereas, others have less computational costs and lower accuracy making them light-weight models. Such models are further modified by the researchers to be more appropriate for better TB diagnosis, termed as Transfer Learning (TL) technique. However, TL leads to complex CNN structure and high computational cost. The proposed model named as Light TBNET(L-TBNET), attempts to provide less computational costs and higher accuracy simultaneously, as compared to other models such as, ShuffleNet, ResNet-50, MobileNet v2, Inception, and DenseNet. Moreover, the proposed does not include TL technique. This is accomplished by combining standard convolutional layers as well as depth-wise separable convolutional layers resulting in a hybrid model. The accuracy of the proposed model is 96% with lesser computational cost. In this way, the model contributes in providing a light-weight CNN model with higher accuracy.