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)