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

• Published in: Journal of environmental and public health 2022, Vol.2022 (1), p.8670534-8670534
• Main Authors: Akram Abdulrazzaq, Ammar, Sulaiman Hamid, Sana, Al-Douri, Asaad T., Hamad Mohamad, A. A., Selvi, D., Mohamed Ibrahim, Abdelrahman
• Format: Article
• Language: English
• Subjects: Absorbance; Algorithms; Anomalies; Artificial intelligence; Biopsy; Cancer; Classification; Colon; Colon cancer; Colorectal cancer; Computer applications; Computer graphics; Deep learning; Diagnosis; Discriminant analysis; Geospatial data; Graphics software; Health care; Hyperspectral imaging; Image analysis; Image classification; Image processing; Imaging techniques; Inflammation; Infrared radiation; Intestine; Large intestine; Machine learning; Medical electronics; Medical imaging; Medical imaging equipment; Medical research; Neural networks; Paraffin; Paraffins; Photons; Pixels; Spectrum analysis; Thyroid cancer; Tissue analysis; Tissues; Tumors; Wavelet transforms
• ISSN: 1687-9805; 1687-9813
• DOI: 10.1155/2022/8670534

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.