Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing.

ABSTRACT: Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap smear image. The majority of existing work cover binary classification approaches using various classifiers and Convolution Neural Networks. However, they suffer from inherent challenges for minute feature extraction and precise classification. We propose a novel methodology to carry out the multiclass classification of cervical cells from Whole Slide Images (WSI) with optimum feature extraction

SUBMITTER: Bhatt AR 

PROVIDER: S-EPMC7959623 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature