Advances in Fuzzy-Based Internet of Medical Things (IoMT)

9An Enhanced Fuzzy Deep Learning (IFDL) Model for Pap-Smear Cell Image Classification

Rakesh S.¹*, Smrita Barua², D. Anitha Kumari³ and Naresh E.^4†

¹Department of BCA, Nitte Institute of Professional Education, Nitte University, Mangalore, India

²Department of Agricultural Statistics, Assam Agricultural University, Jorhat, Assam, India

³Department of CSM, TKRCET, Meerpet, Telangana, India

⁴Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

Abstract

The conventional method of categorizing cervical cancer types relies heavily on the expertise of pathologists, which is associated with a lower degree of precision. The utilization of colposcopy is an essential element in the prevention of cervical cancer. Colposcopy has been a crucial component in the reduction of cervical cancer frequency and humanity rates over the past five decades, in conjunction with precancer screening and treatment. The rise in workload has resulted in reduced diagnostic efficiency and misdiagnosis during vision screening. The utilization of the convolutional neural network (CNN) model in medical image processing has demonstrated its superior performance in the cervical cancer type within the realm of cavernous learning. The present study puts forth two convolutional neural network architectures based on deep learning for the identification of cervical cancer through the analysis of colposcopy images. The models employed in ...