Convolutional Neural Network Architecture Densenet121 to Identify Tuberculosis - Nugraha, Fajri; S, Sumijan; Sovia, Rini (Universitas Putra Indonesia YPTK Padang, 2025)
Smoking habits and the normalization of smoking activities are often a problem in many developing countries in the world. Cigarette smoke can cause many health problems that increase the risk of developing diseases and worsen the condition of people with the disease, one of which is Tuberculosis (TB). In Indonesia, based on the WHO Global TB Report 2024, Indonesia ranks second in the world in TB cases, it is estimated that there are more than 1,000,000 new cases every year, this disease is a very serious health problem and has obstacles in the identification process. This research aims to develop a TB disease identification system using Deep Learning. The methods used in this study are Convolutional Neural Network (CNN) and Densenet121 architecture. Convolutional Neural Network (CNN) was chosen for its ability to perform X-ray image analysis for visual validation, while Densenet121 was chosen because of its flexible architecture that can be applied to a wide range of computer vision applications, including image classification, object identification, and semantic segmentation. The research stage includes data collection, then preprocessing the image, namely resize, normalization, and conversion to arrays, then building a Convolutional Neural Network model with the selected architecture, then model training, model performance evaluation using accuracy and AUC metrics and ending with testing and validation by experts. The dataset used in this study is X-Ray data of tuberculosis patients taken from Kaggle to build a Deep Learning model that is able to identify TB through 100 chest X-ray image datasets. The results of the study show that the CNN model is able to identify tuberculosis with an accuracy rate of up to 90%, so it can help speed up early diagnosis or screening so that patients can continue to receive treatment and treatment. Therefore, the application of deep learning with the Convolutional Neural Network (CNN) method and DenseNet121 architecture based on X-Ray image data is an effective approach in the early detection of tuberculosis and seeks to make an important contribution to the control of lung diseases related to exposure to cigarette smoke in Indonesia.