Abstract
Colorectal cancer become a significant public health issue and is the world’s second leading cause of death from cancer. Cancer becomes a very dangerous disease, because it gives no visible signs at an early stage. Signs of cancer will usually only be seen if it is in the third stadium or the last stadium, where the cancer has spread to surrounding organs. Early diagnosis of colorectal cancer is highly needed because treatment choices are decided and the period of survival is heavily affected. This paper proposes the Convolutional Neural Network (CNN) for detecting four classes of colon cancer. The data-set consists of 2500 images, divided into Tumor, Complex, Lymphoma and Stroma. This data set represents a selection from the Institute of Pathology, University of Heidelberg, Germany, consist of 150 × 150 px textures in histological pictures. The proposed system consist of two hidden convolutional layers, a fully connected layer and use Adam Optimizer with learning 0.001, and trained 10 times (epochs = 10). The result of the proposed system is 83% accuracy.
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Ibrahim, N., Pratiwi, N.K.C., Pramudito, M.A., Taliningsih, F.F. (2021). Non-Complex CNN Models for Colorectal Cancer (CRC) Classification Based on Histological Images. In: Triwiyanto, Nugroho, H.A., Rizal, A., Caesarendra, W. (eds) Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering, vol 746. Springer, Singapore. https://doi.org/10.1007/978-981-33-6926-9_44
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DOI: https://doi.org/10.1007/978-981-33-6926-9_44
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