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All Journal Jurnal Jurusan Ilmu Keolahragaan ELKHA : Jurnal Teknik Elektro Journal of Software Engineering Journal of Intelligent Systems JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Musamus Journal of Primary Education Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Asean Journal of Sport for Development and Peace Indonesian Research Journal on Education Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics Sriwijaya Journal of Sport ACPES Journal of Physical Education, Sport, and Health
Catur Supriyanto
Universitas Dian Nuswantoro
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Journal : Scientific Journal of Informatics

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Optimizing Deep Learning Models with Custom ReLU for Breast Cancer Histopathology Image Classification Nugroho, Wahyu Adi; Supriyanto, Catur; Pujiono, Pujiono; Shidik, Guruh Fajar
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.12722

Abstract

Purpose: The prompt identification of breast cancer is crucial in preventing the considerable damage inflicted by this dangerous form of cancer, which is widely happened across the globe. This study seeks to refine the efficacy of a deep learning-driven approach for the precise diagnosis of breast cancer by employing diverse bespoke Rectified Linear Units (ReLU) to improve the model's performance and reduce inaccuracies within the system. Method: This study focuses on analyzing a deep learning approach utilizing the BreakHis dataset with 7,909 images, incorporating changes to the ReLU activation function across different pre-trained CNN models. It then evaluates performance through measurement such as accuracy, precision, recall, and F1-Score. Result: Based on our experiment results, it can be shown that the DenseNet201 models with a custom LeakyReLU excel beyond the typical ReLU, achieving the highest accuracy, recall, and F1-Score at 99.21%, 99.21%, and 99.11%, respectively. Simultaneously, ResNet152, utilizing LessNegativeReLU (α=0.05), achieved the highest precision at 99.11%. The VGG11 model exhibited the most notable performance enhancement, with improvements ranging from 1.39% to 1.59%. Novelty: The research is original in optimizing a model for accurate breast cancer diagnosis. The proposed model is superior to the model utilizing the default activation function. This finding indicates that the study significantly enhances performance while effectively minimizing errors, thereby necessitating further exploration into the effectiveness of the customized activation function when applied to other medical imaging modalities.
Co-Authors . Safuan, . Abdollah, Mohd. Faizal Abdul Rachman Syam Tuasikal Abu Salam Ahmed, Foez Al Fahreza, Muhammad Daffa Alamsyah, Sayyidul Aulia Amalia Amalia Amalia, Syafira Rosa Amiral, Afinzaki Andreas Wilson Setiawan Antony Eka Aditya, Antony Eka Ardytha Luthfiarta Astuti, Yani Parti Bahauddin, Muhammad Arja Bayu Satria, Zaky Indra Darmawan, Immanuel Julius Dyan Yuliana Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Sri Rahayu, Erna Sri Erwin Yudi Hidayat Etika Kartikadarma Fauzi Adi Rafrastara Fitriyani, Shelomita Gede Doddy Tisna MS Guruh Fajar Shidik Hapsari Peni Heru Agus Santoso HIMAWAN WISMANADI Hussein, Jasim Nadheer Ika Novita Dewi Junta Zeniarja Kafrawi, Fatkur Rohman Khuddus, Lutfhi Abdil Kurniawan, Defri Lin, wei Jhe Liya Umaroh, Liya Marjuni, Aris Mohammad Reza Maulana, Mohammad Reza Muchamad Arif Al Ardha Muljono Muljono Mulyanto, Edy Nining Widyah Kusnanik Nurhasan Nurhasan, Nurhasan Octaviani, Dhita Aulia Oman Somantri Paramita, Cinantya Pitaloka, Tia Amika Prabowo, Suryanto Agung Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Rahadian, Arief Ramadhan Rakhmat Sani Rizka Safriyani Rizki, Ainun Zulfikar Romi Satria Wahono Rusdiawan, Afif Rustam, Suhardi Rustam, Suhardi Sabatian, G. M. Dwiko Jaya Safar, Noor Zuraidin Mohd Sindhu Rakasiwi Sudibyo, Usman Sulistyana, Caturia Sasti Swanny Trikajanti Widyaatmadja Syamsiar, Syamsiar T. Sutojo Utomo, Danang Wahyu Wahyu Adi Nugroho Wakhidah, Elfa Wahyu Wildanil Ghozi Winarsih, Nurul Anisa Sri Yang, Chung Bing Yuhantini, Eva Ferdita YUSUF FUAD