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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Stella Juventia Grace
Universitas Muhammadiyah Surakarta
Computer Science & IT Engineering

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PERBANDINGAN CNN, RESNET50, DAN VISION TRANSFORMER UNTUK KLASIFIKASI KANKER PAYUDARA BERBASIS WEB Stella Juventia Grace; Dedi Gunawan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6420

Abstract

This research aims to compare three deep learning algorithm-based image processing models, namely CNN, ResNet50, and Vision Transformer (ViT), in classifying breast cancer based on mammography images. The CBIS-DDSM dataset from Kaggle was used and processed through pre-processing steps such as data cleaning, image resizing, normalization, augmentation, and data splitting into training and testing sets. The models were evaluated using a 5-Fold Cross Validation scheme to ensure performance stability. The results show that ResNet50 achieved the highest accuracy of 97%, followed by CNN at 92%, and Vision Transformer at 71%. All three models were implemented into a web application using Flask to support the automatic diagnosis process. These findings are expected to help develop a faster and more accurate breast cancer detection system for medical professionals.
Co-Authors Dedi Gunawan