Computer-Aided Diagnosis Using Hybrid Technique for Fastened and Accurate Analysis of Tuberculosis Detection with Adaboost and Learning Vector Quantization

Abstract

Background: The concept of tuberculosis diagnosis plays a significant role in the current world since, in accordance with the Global Tuberculosis (TB) Report in 2019, more than one million cases are reported per year in India. Various tests are available even then the chest X-ray is the most significant one, devoid of which the diagnosis will be incomplete. By the usage of computationally designed algorithms, various clinical, as well as diagnostic functions, were built in ancient poster anterior chest radiographs. The Digital image (X-ray) may be an essential medium for examining and annotating patient’s demographics coverage in the screening of TB via chest radiography. Results: Even though several medicines are available to cure TB, diagnosis with accuracy is a major challenge. So, we have introduced a fastened technique with the merged combination of Adaptive Boosting (AdaBoost) and learning vector quantization (LVQ) for determining TB in an easier way with the input chest X-ray image of a person with the aid of computer-aided diagnosis with greatest accuracy, precision, recall and F1 values. This finest technique got an accuracy of 94.73% when compared to the prior conventional methods used such as SVM and Convolutional Neural Network. Conclusions: Tuberculosis detection can be done in a meaningful way with the aid of MATLAB simulation using Computer Aided Diagnosis. The algorithms Adaboost and LVQ works best with the datasets for around 400 chest X-ray images for detecting the normal and abnormal images conditions for the detection of the disease for a patient suspected to have TB, in a fraction of seconds.