2023
DOI: 10.3390/s23094556
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An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading

Abstract: Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-planning processes.The third most common cancer worldwide and the second most common in women is colorectal cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Un… Show more

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Cited by 2 publications
(2 citation statements)
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“…Carcagni et al used colon carcinoma grading by AI to improve the accuracy and grading of biopsies. Deep learning served to speed up the reading of the histopathology and improve accuracy and interobserver inconsistency [21]. Deep learning presents algorithms to reliably predict prognosis from a colonoscopic biopsy.…”
Section: Use Of Deep Learning For Improved Selection Of Patients At H...mentioning
confidence: 99%
“…Carcagni et al used colon carcinoma grading by AI to improve the accuracy and grading of biopsies. Deep learning served to speed up the reading of the histopathology and improve accuracy and interobserver inconsistency [21]. Deep learning presents algorithms to reliably predict prognosis from a colonoscopic biopsy.…”
Section: Use Of Deep Learning For Improved Selection Of Patients At H...mentioning
confidence: 99%
“…Colorectal carcinoma (CRC) stands as the second largest prevalent malignancy in the world [1]. Over the past few years, there has been a decrease in the overall prevalence of CRC, but the occurrence and fatality rates among patients under 50 years old are rising [2,3]. In patients with CRC, metastasis remains the primary cause of death, whereas CRC recurrence is predominantly attributed to residual tumor cells that had metastasized to distant organs before surgery [4].…”
Section: Introductionmentioning
confidence: 99%