Open Access

ARTICLE

Signet Ring Cell Detection from Histological Images Using Deep Learning

Muhammad Faheem Saleem¹, Syed Muhammad Adnan Shah¹, Tahira Nazir¹, Awais Mehmood¹, Marriam Nawaz¹, Muhammad Attique Khan², Seifedine Kadry³, Arnab Majumdar⁴, Orawit Thinnukool^5,*

1 Department of Computer Science, UET, Taxila, 47050, Pakistan
2 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
3 Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway
4 Department of Civil Engineering, Imperial College London, London SW7 2AZ, UK
5 College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand

* Corresponding Author: Orawit Thinnukool. Email:

Computers, Materials & Continua 2022, 72(3), 5985-5997. https://doi.org/10.32604/cmc.2022.023101

Received 27 August 2021; Accepted 29 November 2021; Issue published 21 April 2022

Abstract

Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases. Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task. Automatic detection of SRCs in a patient's body through medical imaging by incorporating computing technologies is a hot topic of research. In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN). In the first step, the input image is fed to Resnet-101 for feature extraction. The extracted feature maps are conveyed to Region Proposal Network (RPN) for the generation of the region of interest (RoI) proposals as well as they are directly conveyed to RoiAlign. Secondly, RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected (FC) network and performs classification along with Bounding Box (bb) generation by using FC layers. The annotations are developed from ground truth (GT) images to perform experimentation on our developed dataset. Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials. We aim to release the employed database soon to assist the improvement in the SRC recognition research area.

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