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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT Prosiding PESAT Majalah Ilmiah Matematika Komputer Prosiding SNATIKA Vol 01 (2011) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ilmiah KOMPUTASI ALQALAM Computer Science and Information Technologies Prosiding SeNTIK STI&K
Muhammad Subali
Jurusan Teknik Informatika, Sekolah Tinggi Teknik Multimedia Cendekia Abditama
Arts Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : International Journal of Electrical and Computer Engineering

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Optimized ResNet-50 framework for mammogram-based breast cancer classification: a comparative evaluation with EfficientNet-B0 Subali, Muhammad; Wisudawati, Lulu Mawaddah; Teresa, Teresa
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1199-1212

Abstract

Breast cancer remains one of the most prevalent malignancies worldwide, underscoring the need for accurate and reliable mammographic interpretation. Computer-aided diagnosis (CAD) based on deep learning has emerged as a promising approach to improve both screening performance and diagnostic consistency, yet fairness-driven comparisons between popular convolutional backbones on public mammogram benchmarks remain limited. This study provides a statistically validated, fairness-driven comparison of two widely used convolutional neural network architectures, ResNet-50 and EfficientNet-B0, for mammogram-based breast cancer classification under a rigorously controlled, clinically motivated protocol. The proposed “optimized ResNet-50” framework is defined by patient-level stratified undersampling, paired 5-fold cross-validation with identical partitions, harmonized augmentation and training configurations, and dual statistical testing (paired t-tests and Wilcoxon signed-rank tests), emphasizing methodological rigor rather than architectural novelty. Across MIAS and CBIS-DDSM benchmarks, the models demonstrated complementary strengths, with EfficientNet-B0 excelling in screening-oriented tasks (normal vs. abnormal) and ResNet-50 offering more robust performance for diagnostic-oriented tasks (benign vs. malignant). These findings highlight the value of fairness-driven evaluation protocols in CAD research and support the feasibility of integrating lightweight convolutional neural networks (CNNs) into tiered clinical workflows, where different backbones are strategically deployed for initial screening and confirmatory assessment.
Co-Authors Andhika Wiranto Andhika Wiranto Aprian Karisman Aprian Karisman Atmaja, Pungky Hari Wira Bintang Eka Putera Christanto Sinambela Christanto Sinambela, Christanto Darryl, Patrick Debyo Saptono Djasiodi Djasri Fauziah Febrydias, Harsya Hasanuddin, Riki Abdillah Imam Purwanto Imam Purwanto Imam Purwanto Lulu Mawaddah Wisudawati Miftah Andriansyah Miftah Andriansyah Miftah Andriansyah, Miftah Naufal, Muhamad Neneng Alawiyah Swelandiah Endah Pratiwi Teresa, Teresa Wisudawati, Lulu Mawadddah Yudi Wijaya, Yudi