Title: Automated classification of cervical cells using integrated VGG-16 CNN model

Authors: Rajesh Yakkundimath; Varsha S. Jadhav; Basavaraj S. Anami

Addresses: Department of Computer Science and Engineering, K.L.E. Institute of Technology, Hubballi 580027, Karnataka, India; Visvesvaraya Technological University, Belagavi 590018, Karnataka, India ' Department of Information Science and Engineering, S.D.M. College of Engineering and Technology, Dharwad 580 002, Karnataka, India; Visvesvaraya Technological University, Belagavi 590018, Karnataka, India ' Department of Computer Science and Engineering, K.L.E. Institute of Technology, Hubballi 580027, Karnataka, India; Visvesvaraya Technological University, Belagavi 590018, Karnataka, India

Abstract: The most popular method for early cervical cancer screening and detection is the Pap-smear. Automatic analysis of Pap-smear images using computer technology will help in the accurate classification of cervical cancer cells. In this paper, a deep learning approach based on VGG-16 convolutional neural network (CNN) model integrated with support vector machine (SVM) classifier is proposed to identify and classify the cervical cells. A deep convolutional generative adversarial network (DCGAN) framework is employed to generate the required synthetic Pap-smear images. The best average classification result of 96.24% is achieved on the held-out dataset comprising 16,124 images belonging to five classes of cervical cells.

Keywords: cervical cancer; Pap-smear images; data augmentation; classification.

DOI: 10.1504/IJMEI.2024.136966

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.2, pp.185 - 197

Published online: 01 Mar 2024 *

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