Bowel Symptoms in Relation to Colorectal Cancer

Adelstein, Barbara‐Ann; Macaskill, Petra; Katelaris, Peter; Irwig, Les

doi:10.1007/978-3-319-65286-3_2

Cited by 1 publication

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References 25 publications

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“…This colorectal cancer is categorized into several types [ 5 ], such as adenocarcinoma (it is the most common, developed in the large intestine and mucus-producing glands), gastrointestinal carcinoid tumor (developed in large intestine hormone-producing cells), gastrointestinal stromal tumor (developed in Cajal interstitial cells), primary colorectal lymphoma (developed in the colon), and sarcoma (developed in blood vessels). This colon or colorectal cancer has several symptoms [ 6 ], such as diarrhea, bleeding in stool, fatigue, unexpected weight loss, and abdominal discomfort. Based on the condition, the main risk factors [ 7 ] are inherited syndromes, family history, sedentary lifestyle, unhealthy diet, obesity, and smoking.…”

Section: Introductionmentioning

confidence: 99%

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care

Wang

2022

Contrast Media & Molecular Imaging

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Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP system uses MRI radio imaging for predicting colorectal cancer. During this process, high- and low-level features are required to examine cancer in an earlier stage. Due to the limitation of the conventional feature extraction process, both features are difficult to extract from cancer suffered locations. Hence, a deep learning system (DLS) is used to examine the entire bowel MRI image to identify the cancer-affected location, feature extraction, and feature training process. Furthermore, the DLS-based IAECP system helps improve the overall colorectal cancer identification accuracy for further process. The derived bowel features are trained by applying the residual convolution network, which minimizes the error between predicted and actual values. Finally, the test query images are compared with the trained image by applying the sum, which is more absolute to the cross-correlation template feature matching (SACC) algorithm. The experimental process is performed using 100,000 histological data sets, which is considered a publicly available data set. Moreover, the introduced method does not use generic features, whereas the deep learning features help improve the overall IAECP prediction rate (99.8%) ratio as predicted at lab-scale analysis.

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Section: Introductionmentioning

confidence: 99%

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care

Wang

2022

Contrast Media & Molecular Imaging

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Bowel Symptoms in Relation to Colorectal Cancer

Cited by 1 publication

References 25 publications

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care

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