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Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care

Authors :
Ammar Akram Abdulrazzaq
Sana Sulaiman Hamid
Asaad T. Al-Douri
A. A. Hamad Mohamad
D. Selvi
Abdelrahman Mohamed Ibrahim
Source :
Journal of Environmental and Public Health. 2022:1-11
Publication Year :
2022
Publisher :
Hindawi Limited, 2022.

Abstract

Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.

Details

ISSN :
16879813 and 16879805
Volume :
2022
Database :
OpenAIRE
Journal :
Journal of Environmental and Public Health
Accession number :
edsair.doi.dedup.....72ed6fa1d3eb72e77c0eabca44777d0c
Full Text :
https://doi.org/10.1155/2022/8670534