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

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.