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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Suhendro Yusuf Irianto
Department of Magister Informatics Engineering, Institute Informatics and Business Darmajaya, Bandar Lampung, Indonesia
Computer Science & IT Education Engineering

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Multi-Class Classification and Segmentation on Kvasir Endoscopic Images Using Deep Learning Methods Rahman Ardi Saputra; Suhendro Yusuf Irianto; Egi Safitri
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.111973

Abstract

This study evaluates the use of deep learning methods for multi-class classification and polyp segmentation on Kvasir endoscopic images. A dual-model approach was employed, where EfficientNet handles multi-class classification and U-Net handles polyp segmentation, each trained and evaluated independently on their respective datasets. The EfficientNet-B0 model achieved high performance, with accuracy, precision, recall, and F1-score values exceeding 91%, demonstrating its effectiveness in detecting various gastrointestinal abnormalities across eight classes. The U-Net model, while showing strong performance in background detection, faced challenges in lesion delineation, achieving a Dice Similarity Coefficient (DSC) of 34.50% and IoU of 20.85%. These results suggest that running both models in parallel on the same input image could provide simultaneous classification and segmentation outputs, offering more comprehensive diagnostic information compared to single-task approaches. This study contributes to the understanding of independent deep learning components that could support AI-based medical decision-making in gastrointestinal endoscopy.
Co-Authors Egi Safitri Rahman Ardi Saputra