Title: Classification of cervical cancer from Pap smear images: a convolutional neural network approach

Authors: Siti Noraini Sulaiman; Ajmal Hadi Ahmad Hishamuddin; Iza Sazanita Isa; Muhammad Khusairi Osman; Zainal Hisham Che Soh

Addresses: Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia; Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM), Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia; Integrative Pharmacogenomics Institute (iPROMISE), UiTM Puncak Alam Campus, Bandar Puncak Alam, Puncak Alam, Selangor, 42300, Malaysia ' Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia ' Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia; Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM), Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia ' Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia; Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM), Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia ' Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia

Abstract: Cervical cancer is a significant global issue, with Pap smear tests being a common screening tool for precancerous stages. This study aims to develop a computer-aided diagnostics system that can classify precancerous cells from Pap smear images. The project employs convolutional neural networks (CNNs) trained using pre-processed images, adaptive fuzzy K-means (AFKM), and fuzzy C-means (FCM) to classify cervical cancer cell data as normal or abnormal. The datasets used in the project include normal, low-grade squamous intraepithelial lesion (LSIL), and high-grade squamous intraepithelial lesion (HSIL) categories. CNN1, CNN2, and CNN3 have been developed and CNN2 was chosen due to its highest accuracy of 87.71%. The CNN2 trained with AFKM outperformed other networks with an accuracy of 89.53%, precision of 0.870, recall of 0.870, specificity of 0.935, and F1-score of 0.870. This study demonstrates the potential of deep learning-based approaches for identifying and classifying cervical cell pre-cancerous stages.

Keywords: cervical cancer; pap smear; convolutional neural network; CNN; adaptive fuzzy k-means; AFKM; fuzzy C-means; FCM; image classification.

DOI: 10.1504/IJISTA.2023.133702

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.3, pp.303 - 319

Received: 16 Mar 2022
Accepted: 06 Jan 2023
Published online: 29 Sep 2023 *

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