Document
Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations
Title of Periodical
Heliyon
Country of Publication
Kingdom of Bahrain
Place Published
Sakhir, Bahrain
Publisher
University of Bahrain
Date Issued
2023
Language
English
Subject
English Abstract
A B S T R A C T:
This research presents a novel approach for cervical cancer detection and segmentation using tissue images with multiple cells. The study employs a novel deep learning architecture based on Mask Region-Based Convolutional Neural Network (RCNN) and statistical analysis. This new architecture enables us to achieve a high percentage of detection and pix-to-pix area segmentation. A mean Average Precision (mAP) higher than 60% for 3-class and 5-class was achieved. In addition, higher F1-scores of 70% for 3-class and 5-class were obtained. This investigation is a collaborative work, where a medical consultant collected the samples from the Papanicolaou (Pap) Smear examination and labeled the cells presented to the liquid-based cytology (LBC). Furthermore, the online available benchmark data set, SIPaKMeD, was also utilized. Additionally, sample images from the Mendeley data set were also labeled by the trained medical consultant for comparison. The proposed scheme automatically generates a full report for a medical consultant to identify the location of the malicious cells in the given images and expedite the diagnosis and treatment process.
Member of
Identifier
https://digitalrepository.uob.edu.bh/id/0d36e6d0-0c9e-4f92-bfc0-ade3f2457b80
Same Subject