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

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.

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

“…Machine learning algorithms, such as deep learning, have been effectively employed for feature extraction and classification recognition of hyperspectral images ( 16 , 80 ). Moreover, the research field has continuously expanded its scope to encompass the classification and recognition of various other subjects including cancer, tumor, and skin ( 64 , 81–83 ), forming a positive development trend that keeps pace with the times.…”

The role of deep learning in diagnosing colorectal cancer

“…What is more, DL via CNN has been proven efficient in extracting further information regarding the cellular characteristics of CRC and spatial features regarding the tumour microenvironment, such as the stroma:tumour ratio and the presence and/or the type of white blood cells infiltrating the peritumoral region, avoiding the inter-observational variability. For this purpose, to avoid artifacts caused by the H&E staining, according to various researchers, infrared microscopy could be a non-inferior alternative; infrared microscopy could enhance details regarding the molecular background of the existing CRC and information concerning the spatial Gastroenterology Review 2023; 18 (3) features of the tumour [41][42][43][44]. For this purpose, both direct infrared microscopic observation of H&E slides or image editing through Fourier transformation have yielded quite promising outcomes, reporting an AUROC up to 0.83 and sensitivity ranging from 69% to 83% depending on the tumour grade [22].…”

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

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”