Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

Tamang, Lakpa Dorje; Kim, Byung Wook

doi:10.3390/app112210982

Cited by 27 publications

(9 citation statements)

References 97 publications

(97 reference statements)

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“…Although only experimental, emerging deep learning technology can autonomously enhance current screening algorithms improving CRC diagnosis and prognosis [ 22 ]. The rising applicability of iVR in medicine is consecutive to exponential technological growth and availability, and it is predicted to become as ubiquitous as smartphones are today [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Review on Immersive Virtual Reality Interventions for Colonoscopy-Induced Anxiety and Pain

Găină

Szalontay

Ştefănescu

et al. 2022

JCM

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Background: Colonoscopy related fear impairs the current gold standard screening of colorectal cancer. Compared to other minimally invasive procedures for cancer screening, colonoscopy-induced anxiety exceeds the procedure through bowel preparation. Immersive virtual reality’s (iVR) role in alleviating the complex stress–pain relationship encountered during medical procedures is directly proportional to the rising affordability of state-of-the-art Head-Mounted-Displays (HMDs). Objective: to assess the effect of iVR on patients’ colonoscopy-induced anxiety and pain. Materials and methods: A systematic search was conducted in PubMed, Cochrane Central Register of Controlled Trials, Web of Science, Embase and Scopus databases up to January 2022. Clinical trials evaluating anxiety as an outcome were included without language restriction. Results: Four clinical trials were included: three on the patients’ intraprocedural anxiety and one on patient education. Intraprocedural iVR interventions for colonoscopy-induced anxiety and pain revealed a similar effect as conventional sedation, while a statistically significant reduction was reported for non-sedated patients. iVR patient education improved the quality of bowel preparation and reduced patient anxiety before colonoscopy. Conclusions: The current research highlights the need to use high-end HMDs and appropriate interactive iVR software content for colonoscopy-induced anxiety. Methodological frameworks regarding the eligibility of participants, double-blinding and randomization of iVR studies can facilitate the development of iVR implementation for anxiety and pain management.

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Section: Introductionmentioning

confidence: 99%

State-of-the-Art Review on Immersive Virtual Reality Interventions for Colonoscopy-Induced Anxiety and Pain

Găină

Szalontay

Ştefănescu

et al. 2022

JCM

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“…The wavelet transform (WT) has been widely employed in examining nonlinear features and kernel-based approaches (KM), such as kernel PCA and discriminant analysis in the Fisher core [ 10 ]. Manual approaches for HSI classification are still widely utilized in tumour diagnosis [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

Abdulrazzaq

Hamid²,

Al-Douri

et al. 2022

Journal of Environmental and Public Health

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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.

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“…Dr. Jonathan Delafield-Butt, a senior professor in childhood development, said it was important to detect autism early so parents and children could receive a variety of support services. [7]. Autism is a neurodevelopmental disorder with many shared traits, challenges, and abilities.…”

Section: B the Role Of Video Games In Autismmentioning

confidence: 99%

Autism Spectrum Disorder Detection: Video Games based Facial Expression Diagnosis using Deep Learning

Derbali¹,

Jarrah²,

Randhawa³

2023

IJACSA

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In this study, a novel method is proposed for determining whether a child between the ages of 3 and 10 has autism spectrum disorder. Video games have the ability to immerse a child in an intense and immersive environment. With the expansion of the gaming industry over the past decade, the availability and customization of games for children has increased dramatically. When children play video games, they may display a variety of facial expressions and emotions. These facial expressions can aid in the diagnosis of autism. Footage of children playing a game may yield a wealth of information regarding behavioral patterns, especially autistic behavior. You can submit any video of a child playing a game to the interface, which is powered by the algorithm presented in this work. We utilized a dataset of 2,536 facial images of autistic and typically developing children for this purpose. The accuracy and loss function are presented to examine the 92.3% accurate prediction outcomes generated by the CNN model and deep learning.

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Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

Cited by 27 publications

References 97 publications

State-of-the-Art Review on Immersive Virtual Reality Interventions for Colonoscopy-Induced Anxiety and Pain

State-of-the-Art Review on Immersive Virtual Reality Interventions for Colonoscopy-Induced Anxiety and Pain

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

Autism Spectrum Disorder Detection: Video Games based Facial Expression Diagnosis using Deep Learning

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