TY - JOUR AU - Melyana Nurul Widyawati, AU - Mochamad Imron, AU - Siti Masrochah, PY - 2020/08/19 Y2 - 2024/03/29 TI - Tuberculosis detection using convolutional neural network JF - International Journal of Allied Medical Sciences and Clinical Research JA - ijamscr VL - 8 IS - 3 SE - Articles DO - 10.61096/ijamscr.v8.iss3.2020.535-546 UR - https://ijamscr.com/ijamscr/article/view/892 SP - 535-546 AB - <p>Background</p><p>Pulmonary tuberculosis is an infectious disease has become one of the ten leading causes of death globally. Increasing the number and variety of radiological examinations increases the workload of radiologists. This causes the radiologist to experience fatigue, and trigger an inaccurate diagnosis, missed or delayed diagnosis. Machine learning is a computational model with an algorithm that is similar to the structure and function of the biological network of the human brain. It's part of artificial intelligence that uses computer science to perform digital image processing with pattern recognition techniques. The algorithm in machine learning can calculate, recognize the pattern in the image, and make predictive diagnoses.</p><p>Objective</p><p>Generate deep learning model that can classify the chest x-rays image as tuberculosis and normal, also have the same performance with radiologists.</p><p>Methods</p><p>The deep learning model using Convolutional Neural Network (CNN) with the input image size and filter size variation has developed, then compared to the expert performance.</p><p>Results</p><p>Obtained the optimum deep learning model using an image of 200 x 200 and 5 x 5 filter size that has an accuracy, sensitivity, specificity, precision, and AUC were 0.97, 0.9667, 0.975, 0.9831, and 0.971 with CI of 0.932-1.</p><p>Conclusion</p><p>The deep learning model has 98% classification similarity with expert has obtained.</p> ER -