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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal RESISTOR (Rekayasa Sistem Komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JATI (Jurnal Mahasiswa Teknik Informatika) Soeropati: Journal of Community Service Melek IT: Information Technology Journal Jurnal Krisnadana Nusantara Journal of Computers and its Applications Kohesi: Jurnal Sains dan Teknologi
Cahya Bagus Sanjaya
Universitas Yudharta Pasuruan
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Comparison of Transfer Learning Model Performance for Breast Cancer Type Classification in Mammogram Images Cahya Bagus Sanjaya; Muhammad Imron Rosadi; Moch. Lutfi; Lukman Hakim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6177

Abstract

Globally, breast cancer is the type of cancer that most women suffer from. Early detection of breast cancer is very important because there is a big chance of cure. Mammography screening makes it possible to detect breast cancer early. The study of computer-assisted breast cancer diagnosis is gaining increasing attention. Breast cancer comes in two forms: benign cancer and malignant cancer. advances in deep learning (DL) technology and its use to overcome obstacles in medical imaging, and classification using a number of transfer learning models to identify the type of breast cancer (malignant, benign, or normal). This work conducted a thorough comparison analysis of eight prevalent pre-trained CNN algorithms (VGG16, ResNet50, AlexNet, MobileNetV2, ShuffleNet, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2) for breast cancer classification. In this study, we permonData is divided into training, testing, and validation. Using the publicly accessible mini-DDSM dataset, we assess the proposed architecture. were used to measure the classification accuracy (Acc). For genBased on test results, the best accuracy was obtained using EfficientNetb2 with an accuracy value of 94% for training data and 98% for test data on mammogram images.
Co-Authors Abdillah, Achmad Sya'roni Ahwan, Zainul Aldi, Akhmad Anggy Jovano Daniel Oranova Siahaan Lukman Hakim Lutfi, Moch. M. Nufail Magfirah Putri, Yulia Moch. Lutfi Mufidah, Atik Hidayati Muhammad Fahmi Aziz Muhammad Imron Rosadi Muhammad Imron Rosadi Muhammad Sofyan Mukhamad Murtadho Riska Putri Anggraeni, May Tantri Ichtia Ningsih