Implementation and Optimization Of Inception Resnet-v2 with Data Balancing (Case Study Of Lung Disease Classification)

Abstract

Lungs are vital organs in humans because the process of breathing in humans occurs through the lung organs. However, there are diseases of the lungs, such as COVID-19, Pneumonia, and Tuberculosis that can disrupt the respiratory system in humans. Early detection is required by chest x-ray examination. The development of artificial intelligence technology can help classify chest x-rays with image analysis. In classification for image analysis, there are problems where the data is not balanced, which can cause errors in classification. Thus, data balancing is needed to balance the data. This study conducted training using InceptionResnet-v2 with data balancing. The best model performance results were obtained by training using random oversampling on the model using epoch 20, batch size 64, and learning rate 0.0001 with an accuracy value of 89.23%, loss 0.28, precision 90.05%, recall 89.88%, F1-score 89.74%, and AUC 98%. The accuracy value increased by 7.52% compared to the imbalanced dataset and by 1.29% compared to the random undersampling dataset. Abstract.