• Muhamad Fathurahman Universitas YARSI
  • Rachmadhani Ajeng Nurmufti Universitas YARSI
  • Elan Suherlan Universitas YARSI



CNN, Klasifikasi, Sel, Neural Network, Pap-smear


The classification of cell types plays an essential role in monitoring the growth of cancer cells. One of the methods to determine the cancer type is to analyze the pap-smear images manually. Nevertheless, the manual analysis of pap-smear images by the expert has several limitations, such as time-consuming and prone to misdiagnosis. For reducing the risks, it requires the automatic classification of cell types based on pap-smear images. This study utilizes the convolutional neural network (CNN) architectures to automatically classify the cell type into two-class categories (normal/abnormal) based on three features. These features, such as the local binary pattern, gray level co-occurrence matrix, and shape features, are extracted from pap-smear images. This study shows the performance of CNN achieved the maximum accuracy of 99.98%, 100.0%, 99.78% in training, validation, and testing data. Our approach also outperforms the performance of the baseline methods.    

Keywords : CNN, Classification, Cell, Neural Network, Pap-smear

Author Biographies

Muhamad Fathurahman, Universitas YARSI

Program Studi Teknik Informatika
Fakultas Teknologi Informasi

Rachmadhani Ajeng Nurmufti, Universitas YARSI

Program Studi Teknik Informatika
Fakultas Teknologi Informasi


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