DIAGNOSIS OF GASTROINTESTINAL CANCER METASTASIS WITH DEEP LEARNING TECHNIQUE

Document Type : Original Article

Authors

1 Communication and Electronics Department, Faculty of Engineering, Al-Azhar University, Nasr City, 11884, Cairo, Egypt

2 Department of Electronics, National Telecommunications Institute, Nasr City, 11884, Cairo, Egypt

Abstract

Gastrointestinal cancer is a leading cause of cancer-related deaths globally, prompting significant research into using artificial intelligence (AI) for detection. Researchers have been exploring AI applications in this field since the 1960s, leveraging its ability to handle repetitive tasks and complex computations. In Phase I of this study, various AI models, including basic CNN and more advanced ones like vgg16, Alex, and DenseNet121, were employed to diagnose gastrointestinal cancer using datasets comprising images of benign tumors and malignancies from patients. However, accuracy rates with conventional techniques were found to be insufficient. Thus, Phase II focused on refining the DenseNet121 model, leading to improved accuracy, sensitivity, and specificity. The modified model demonstrated enhanced diagnostic performance, albeit with slightly longer processing times, compared to existing approaches.
 
Special Issue of AEIC 2024 (Electrical and System & Computer Engineering  Session)

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Journal of Al-Azhar University Engineering Sector
Volume 19, Issue 72
Special Issue for Al-Azhar Engineering 16th. International Conference (AEIC 2024)
July 2024
Pages 333-350

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