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Advances in Nano Research Volume 15, Number 5, November 2023 , pages 451-466 DOI: https://doi.org/10.12989/anr.2023.15.5.451 |
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Nanotechnology in early diagnosis of gastro intestinal cancer surgery through CNN and ANN-extreme gradient boosting |
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Y. Wenjing, T. Yuhan, Y. Zhiang, T. Shanhui, L. Shijun, M. Sharaf
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| Abstract | ||
| Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean ± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture. | ||
| Key Words | ||
| artificial neural networks; convolutional neural networks; extreme gradient boosting; gastrointestinal cancer; nanotechnology | ||
| Address | ||
| Y. Wenjing, T. Yuhan, Y. Zhiang, T. Shanhui, L. Shijun: 1Institute of Life Science, Wenzhou University, Wenzhou, China M. Sharaf: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia | ||