<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Colorectal cancer (CC) is prevalently studied carcinoma and it grows in the colon area of huge intestine. Histopathologist Usually Examine the colon biopsy during surgery or colonoscopy. Initial recognition of CC is useful for maintaining the model of accumulated cancerous cells. In medicinal practices, histopathological study of tissue specimens usually occurs in a traditional method but automatic tools which utilize Artificial Intelligence (AI) systems create effectual outcomes in disease detection efficiency. Deep learning (DL)techniques are demonstrated to generate remarkable outcomes on histopathology images in several studies. This study presents a Snake Optimization with Deep Learning Enabled Disease Detection Model for Colorectal Cancer (SODL-DDCC). The presented SODL-DDCC technique concentrates on the identification of CC on histopathological images. In the preliminary stage, the presented SODL-DDCC technique executes bilateral filtering (BF) approach to remove noise. In addition, the presented SODL-DDCC technique exploits Inception v3 as a feature extracting model with SO algorithm as a hyperparameter maximizing process. For CC classification, the graph convolution network (GCN) model is exploited. The investigation outcome evaluation of the SODL-DDCC approach is evaluated on standard dataset and the outputs are evaluated under distinct features. The empirical outputs highlighted the enhancements of the SODL-DDCC over current approaches.</p>
</abstract>

Colorectal cancer (CC) is prevalently studied carcinoma and it grows in the colon area of huge intestine. Histopathologist Usually Examine the colon biopsy during surgery or colonoscopy. Initial recognition of CC is useful for maintaining the model of accumulated cancerous cells. In medicinal practices, histopathological study of tissue specimens usually occurs in a traditional method but automatic tools which utilize Artificial Intelligence (AI) systems create effectual outcomes in disease detection efficiency. Deep learning (DL)techniques are demonstrated to generate remarkable outcomes on histopathology images in several studies. This study presents a Snake Optimization with Deep Learning Enabled Disease Detection Model for Colorectal Cancer (SODL-DDCC). The presented SODL-DDCC technique concentrates on the identification of CC on histopathological images. In the preliminary stage, the presented SODL-DDCC technique executes bilateral filtering (BF) approach to remove noise. In addition, the presented SODL-DDCC technique exploits Inception v3 as a feature extracting model with SO algorithm as a hyperparameter maximizing process. For CC classification, the graph convolution network (GCN) model is exploited. The investigation outcome evaluation of the SODL-DDCC approach is evaluated on standard dataset and the outputs are evaluated under distinct features. The empirical outputs highlighted the enhancements of the SODL-DDCC over current approaches.

Snake Optimization with deep learning enabled disease detection model for colorectal cancer

Journal of Smart Internet of Things

This work is licensed under the Creative Commons Attribution 4.0 International License.

Colorectal cancer (CC) is prevalently studied carcinoma and it grows in the colon area of huge intestine. Histopathologist Usually Examine the colon biopsy...

Snake Optimization with deep learning enabled disease detection model for colorectal cancer

Online veröffentlicht: 15. Dez. 2022

Seitenbereich: 178 - 195

Eingereicht: 05. Nov. 2022

Akzeptiert: 25. Nov. 2022

DOI: https://doi.org/10.2478/jsiot-2022-0012

Schlüsselwörter
Colorectal Cancer, Disease detection, Medical imaging, Deep learning, Snake Optimizer

© 2022 Kassem AL-Attabi, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Snake Optimization with deep learning enabled disease detection model for colorectal cancer

Online veröffentlicht: 15. Dez. 2022

Seitenbereich: 178 - 195

Eingereicht: 05. Nov. 2022

Akzeptiert: 25. Nov. 2022

DOI: https://doi.org/10.2478/jsiot-2022-0012

SchlüsselwörterColorectal Cancer, Disease detection, Medical imaging, Deep learning, Snake Optimizer

© 2022 Kassem AL-Attabi, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Colorectal Cancer, Disease detection, Medical imaging, Deep learning, Snake Optimizer