Comparison of Convolution Neural Network Architecture for Colon Cancer Classification
DOI:
https://doi.org/10.3991/ijoe.v18i03.27777Keywords:
classification, colon cancer, CNN, pre-trained networkAbstract
In 2021, colon cancer is the second most common cause of death for this type of cancer. Therefore, in this study, a colon cancer classification system was developed to help medical staff classify 2 types of cancer colon adenocarcinomas and benign colonic tissues. The classification method uses the Convolution Neural Network (CNN) with the architecture VGG16, VGG19, ResNet101, ResNet152, MobileNetV2, DenseNet201 and InceptionV3. We used 10.000 image datasets that divided into 7200 training data, 1800 validation data and 1000 test data. Pre-trained models are used to extract new features and training data. The best performance parameter based on accuracy, precision, recall and f1-score and confusion matrix are obtained in 3 architectures, namely VGG19, ResNet101 and ResNet152. These architectures can identify and classify both types of colon cancer with 100% accuracy.
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Copyright (c) 2022 Indrarini Dyah Irawati, Sugondo Hadiyoso
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