scholarly journals Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

2022 ◽  
Vol 12 (1) ◽  
pp. 738
Author(s):  
Akella S. Narasimha Raju ◽  
Kayalvizhi Jayavel ◽  
Tulasi Rajalakshmi
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Premalignant Lesions ◽  
Computer Assisted ◽  
Screening Methods ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>

An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

2018 ◽  
Vol 9 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Extraction Methods ◽  
Learning Technologies ◽  
Computer Assisted ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Histological Images ◽  
Assisted Diagnosis ◽  
The Cost

Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.

An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models

2020 ◽  
pp. 684-703
Author(s):  
Shamik Tiwari
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Extraction Methods ◽  
Learning Technologies ◽  
Computer Assisted ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Histological Images ◽  
Assisted Diagnosis ◽  
The Cost

Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.

scholarly journals Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

2020 ◽  
Vol 9 (10) ◽  
pp. 3313 ◽  
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Aman Ali ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Narrow Band Imaging ◽  
Screening Methods ◽  
Polyp Detection ◽  
Mortality And Morbidity ◽  
Magnifying Chromoendoscopy

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

574 RECENT TRENDS IN COLORECTAL CANCER SCREENING METHODS BASED ON MEDICARE CLAIMS DATA

2020 ◽  
Vol 158 (6) ◽  
pp. S-121
Author(s):  
Ahmet B. Ozbay ◽  
Lila J. Finney Rutten ◽  
John B. Kisiel ◽  
Paul Limburg ◽  
Marcus Parton
Keyword(s):  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Claims Data ◽  
Screening Methods ◽  
Medicare Claims ◽  
Recent Trends ◽  
Medicare Claims Data

Mo1627 - Deep Learning with Big Clinical Data for Prediction of Advanced Neoplasia in Colorectal Cancer Screening of Asymptomatic Adults

2018 ◽  
Vol 154 (6) ◽  
pp. S-774
Author(s):  
Hyo-Joon Yang ◽  
Chang Woo Cho ◽  
Sang Soo Kim ◽  
Kwang-Sung Ahn ◽  
Soo-Kyung Park ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Clinical Data ◽  
Advanced Neoplasia

Implementing a computer-assisted telephone interview (CATI) system to increase colorectal cancer screening: A process evaluation

2006 ◽  
Vol 61 (3) ◽  
pp. 419-428 ◽  
Author(s):  
Mary Jo White ◽  
Jennifer R. Stark ◽  
Roger Luckmann ◽  
Milagros C. Rosal ◽  
Lynn Clemow ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Process Evaluation ◽  
Telephone Interview ◽  
Computer Assisted ◽  
Computer Assisted Telephone Interview

scholarly journals Modeling Individual Patient Preferences for Colorectal Cancer Screening Based on Their Tolerance for Complications Risk

2016 ◽  
Vol 37 (3) ◽  
pp. 204-215 ◽  
Author(s):  
Glen B. Taksler ◽  
Adam T. Perzynski ◽  
Michael W. Kattan
Keyword(s):  
Colorectal Cancer ◽  
Cancer Screening ◽  
Life Expectancy ◽  
Colorectal Cancer Screening ◽  
Flexible Sigmoidoscopy ◽  
Screening Method ◽  
Screening Strategy ◽  
Screening Methods ◽  
Average Risk ◽  
National Guidelines

Introduction. Recommendations for colorectal cancer screening encourage patients to choose among various screening methods based on individual preferences for benefits, risks, screening frequency, and discomfort. We devised a model to illustrate how individuals with varying tolerance for screening complications risk might decide on their preferred screening strategy. Methods. We developed a discrete-time Markov mathematical model that allowed hypothetical individuals to maximize expected lifetime utility by selecting screening method, start age, stop age, and frequency. Individuals could choose from stool-based testing every 1 to 3 years, flexible sigmoidoscopy every 1 to 20 years with annual stool-based testing, colonoscopy every 1 to 20 years, or no screening. We compared the life expectancy gained from the chosen strategy with the life expectancy available from a benchmark strategy of decennial colonoscopy. Results. For an individual at average risk of colorectal cancer who was risk neutral with respect to screening complications (and therefore was willing to undergo screening if it would actuarially increase life expectancy), the model predicted that he or she would choose colonoscopy every 10 years, from age 53 to 73 years, consistent with national guidelines. For a similar individual who was moderately averse to screening complications risk (and therefore required a greater increase in life expectancy to accept potential risks of colonoscopy), the model predicted that he or she would prefer flexible sigmoidoscopy every 12 years with annual stool-based testing, with 93% of the life expectancy benefit of decennial colonoscopy. For an individual with higher risk aversion, the model predicted that he or she would prefer 2 lifetime flexible sigmoidoscopies, 20 years apart, with 70% of the life expectancy benefit of decennial colonoscopy. Conclusion. Mathematical models may formalize how individuals with different risk attitudes choose between various guideline-recommended colorectal cancer screening strategies.

scholarly journals Colorectal cancer screening methods

2016 ◽  
Vol 32 (3) ◽  
pp. 1453-1458
Author(s):  
Dusan Popovic ◽  
Tamara Alempijevic ◽  
Nada Kovacevic ◽  
Milan Spuran ◽  
Dragan Tomic ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Screening Methods
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