Detection of Tuberculosis in Chect X-Ray using Cancatinated Deep and Handcrafted Features

The lung infection in human causes various respiratory problems, which affects the oxygen supply in blood stream. Tuberculosis (TB) is a severe lung disease in humans and the uncontrolled TB causes various respiratory problems, including death. TB is also a communicable disease and appropriate diagnosis and treatment will reduce the severity of the disease. This research aims to implement a novel disease diagnosis procedure to detect the TB infection in chest X-ray with better accuracy. This research employs the concatenation of deep-features (DF) with handcrafted-features (HF) to improve the diagnostic accuracy. In this work, the VGG16 is employed to extort the DF and the HF is obtained using the discrete-wavelet-transform (DWT) approach. The optimal values of DF and HF are arranged as per their rank and then a serial feature concatenation is employed to get a new 1D feature (DF+HF). This feature is then considered to train and validate the performance of considered classifiers using a 5-fold cross validation and it offered an accuracy of >97% with the Fine-Tree classifier.