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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor JUITA : Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Computer Science and Information Technologies Jurnal Ilmiah Matematika
Aris Thobirin, Aris
University of Ahmad Dahlan
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Implementation of DenseNet121 Based on Convolutional Neural1 Network with Geometric Augmentation for Breast Cancer2 Histopathology Image Classification Ariani, Nabilah Evi; Surono, Sugiyarto; Thobirin, Aris
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37896

Abstract

The development of a robust deep learning architecture that is not easily affected by overfitting is an important factor in improving the performance of medical image classification systems. This study aims to assess the ability of the DenseNet121 architecture to classify histopathological images into two categories. The model utilizes pre-trained weights from ImageNet and is adjusted through fine-tuning, while geometric data augmentation techniques are performed to increase sample variation. The training process utilizes the AdamW optimizer and the Binary Cross-Entropy loss function, with performance assessment using binary classification metrics. The test results show that DenseNet121 achieved a training accuracy of 98.96%, a validation accuracy of 97.72%, and a testing accuracy of 97.09%, indicating consistent performance at each stage and no signs of overfitting. This finding indicates that DenseNet121 has great potential as an effective structure in histopathological image classification systems.
Co-Authors Abdillah, Alfiyyah 'Ainul Abdulnazar, Mohamed Naeem Antharathara Ariani, Nabilah Evi Dewi, Deshinta Arrova Eko Prasetyo Firdausy, Salma Nadya Hanifa, Riza Aulia Ibad, Irsyadul Ida Ayu Putu Sri Widnyani khusnul khotimah Larasati, Anggit Mujhid, Almuzhidul Rahmadani, Lanova Ade Rismawati, Dewi Salsabila, Sona Regina Subrata, Arsyad Cahya Sugiyarto Surono, Sugiyarto Suris, Badzliana Aqmar Surono , Sugiyarto Wijayanti, Dian Eka