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Communication Papers of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 45

Enhanced GI Tract Cancer Diagnosis Using CNNs and Machine Learning Models

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DOI: http://dx.doi.org/10.15439/2025F8672

Citation: Communication Papers of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 45, pages 77–84 ()

Full text

Abstract. Among all malignancies, gastrointestinal cancer has the highest fatality rate at 35.4\%. One of the few ways to get GI tract lesions images is endoscopy. Manual cancer detection is laborious. Deep learning can diagnose GI tract lesions automatically. Automation yields inaccurate detection findings. The difficult Hyper-Kvasir dataset is utilized for training and validation in this work. The procedure has several phases. The dataset is initially preprocessed using Brightness Preserving Histogram Equalization. Additionally, processed datasets contain training and validation sets. The network also receives training sets. Segmentation uses U-Net architecture. U-Net backbones are Efficientnet-B0, B7, and Densenet201. The pre-trained models are trained on ImageNet, therefore Hyper-Kvasir is used to fine-tune GI tract segmentation. Semantic segmentation yields result. Best IoU is 85.2\% for Efficientnet-B7 backbone in U-Net architecture. Using a custom convolutional neural network, hyper kvasir classification dataset is classified. The proposed network extracts deep characteristics for artificial neural network categorization. Compared to state-of-the-art (SOTA) approaches, the suggested methodology outperformed them.

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