Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Contrast Stretching Pada CNN dengan EfficientNet-B0
DOI:
https://doi.org/10.30865/klik.v4i3.1448Keywords:
Lung Cancer; CNN; EfficientNet-B0; Classification; Contrast StretchingAbstract
Data from the World Health Organization (WHO) indicates that in 2020, approximately 10 million people died from cancer. Smoking has been identified as a primary factor causing lung cancer, as cigarettes contain over 60 toxic substances that can trigger the development of the disease. The rate of lung cancer has rapidly increased due to excessive cigarette consumption. Detecting nodules in the lungs typically takes about 10-30 minutes. In this study, a Convolutional Neural Network (CNN) algorithm with EfficientNet-B0 architecture is employed to classify lung cancer. The preprocessing process involves contrast stretching, and various hyperparameter optimization techniques such as Adam, Adagrad, and SGD are used to enhance the CNN's performance. Average pooling with output dense layers of 64, 32, 16, 1 is utilized. Performance analysis is conducted using a confusion matrix. The highest classification results are achieved using the ADAM optimizer with a learning rate of 0.01, where accuracy reaches 72.48%, precision is 71.52%, recall is 64.2%, and the F1 score is 64.76%. Meanwhile, results obtained from the original dataset show differences. The highest classification result is obtained using the ADAM optimizer with a learning rate of 0.01, achieving an accuracy of 64.22%, precision of 52.69%, recall of 50.52%, and an F1 score of 43.51%. These results indicate that the use of contrast stretching in lung cancer classification preprocessing is highly effective in improving accuracy
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