Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Image Enhancement CLAHE Pada EfficientNet-B0 - Dzaky Abdillah Salafy; Febi Yanto; Surya Agustian; Fitri Insani (STMIK Budi Darma, 2023)
In recent years, there has been a significant increase in the global cancer related mortality rate. Among various cancer types, lung cancer has emerged as one of the highest incidence cases. Lung cancer predominantly affects males and is attributed to several factors, including exposure to cigarette smoke, long-term air pollution, and exposure to carcinogenic compounds such as radon, asbestos, arsenic, coal tar, and diesel fuel emissions. The growth of cancerous cells in the lungs can be detected using various imaging techniques, with CT-Scan being one of them. This research focuses on the classification of normal lung organs and those affected by cancerous cells. The classification process employs two types of data: original data and data processed with Contrast Limited Adaptive Histogram Equalization (CLAHE). The data is initially divided with 90:10 ratios before being trained using a Convolutional Neural Network (CNN). The CNN architecture used is EfficientNet-B0, with the assistance of different optimizers and learning rates. After testing, the model's performance is evaluated using a confusion matrix to compare the results between the use of original data and CLAHE-processed data. The use of CLAHE processed data yields higher evaluation metrics compared to the original data, achieving a precision of 87.9%, recall of 85.6%, F1-score of 85.11%, and accuracy of 85.29% in the 90:10 data split, with the Adam optimizer and a learning rate of 10-1. The research results reveal that the utilization of image enhancement, specifically Contrast Limited Adaptive Histogram Equalization (CLAHE), with an appropriate combination of clip limit and tile grid, can impact the model's performance in classifying image data.