Classification of Tuberculosis using a Convolutional Neural Network - Zamah Sari; Yufis Azhar (Jurnal Repositor, 2023)
Tuberculosis disease causes the death of 1.8 million people worldwide, according to data from the World Health Organization in 2018 that nearly 10 million people were affected by tuberculosis and about 98,000 of them died. Technological developments, especially computer engineering, have helped accelerate the diagnosis of tuberculosis in poverty source areas. One of them is Computer Vision and Machine Learning Technology which causes development and utilization in all aspects, including the health sector. In the development of computer vision, there is a deep learning technique, this technique can automatically detect and classify various diseases with better accuracy. One method of deep learning that can produce precise accuracy and better efficiency is using a Convolutional Neural Network (CNN). This study uses CNN in the classification of the Tuberculosis Chest X-ray Database. The study was carried out in 4 scenarios, with each scenario getting accuracy results of 70%, 97%, 97%, and 72%..