Computer-Aided Diagnosis using Hybrid Technique for Fastened and Accurate Analysis of Tuberculosis Detection with Adaboost And Learning Vector Quantization
The concept of tuberculosis detection paves a major role in this recent world because, according to the Global Tuberculosis (TB) Report in 2019, more than one million cases are reported per year in India. Even though various tests are available, the chest X-ray is the most important one, without which the detection will be incomplete. In ancient poster anterior chest radiographs, several clinical and diagnostic functions are built by the use of computationally designed algorithms. These algorithms assist in the scientific diagnostic analysis by victimization acquisition of pictures. The Digital image may be a necessary medium for analyzing, annotating, and patient's demographics coverage in the screening of TB via chest radiography. As the diagnosis of TB is the major challenge here, we have introduced a fastened technique with the merged combination of AdaBoost and learning vector Quantization for determining TB in an easier way with the input chest X-ray image of a person with the aid of computer-aided diagnosis.
This is my original work and is not published elsewhere.