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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Keteknikan Pertanian Tropis dan Biosistem Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jiko (Jurnal Informatika dan komputer) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknik Informatika (JUTIF) Jurnal Informatika dan Multimedia
Yuita Arum Sari
Brawijaya University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mechanical Engineering Social Sciences Other

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Improving Direct Image Regression for Blood Cell Enumeration with a Fine-Tuned Backbone Adinugroho, Sigit; Sari, Yuita Arum; Utaminingrum, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5004

Abstract

Complete blood count (CBC) examination provides an important insight for diagnosis or disease treatment. Currently, CBC examination requires complex and expensive devices that limit their deployment in remote area. The development of computer vision based method offers simplification to the process. However, its implementation is limited to the availability of large size labelled dataset. This research aims to develop a direct image regressor that is able to regress directly from image. There are two stages in estimation process. First, the backbone is trained using large dataset available for blood cell classification problem. Then the trained backbone is plugged into the final model by adding a fully connected neural network that acts as regressor. The whole model is then trained using limited whole blood cell count dataset. The evaluation process shows that training the backbone using large size related dataset improve the performance by 50%. This study can be used to create a low-cost blood component evaluation tool, particularly in rural areas where access to advanced laboratory equipment is limited.
Co-Authors Achmad Arwan Adinugroho, Sigit Agus Wahyu Widodo, Agus Wahyu Agus Zainal Arifin Alayasi, Mutia Alfisyar Jefry Pranata Anggi Gustiningsih Hapsani Anggi Gustiningsih Hapsani Bihanda, Yusuf Gladiensyah Budi Darma Setiawan Chastine Fatichah Dahnial Syauqy Dewa Gede Trika Meranggi Dhea Rahma Widyadhana Edy Santoso Evy Kamilah Ratnasari Fadhil Yusuf Rahadika Fatchurrahman, Danial Hapsani, Anggi Gustiningsih Hurriyatul Fitriyah Indriati Indriati Jaya Mahar Maligan Jaya Mahar Maligan Jayawarsa, A.A. Ketut Laksono Trisnantoro Lukman Hakim Luthfi Maulana, Luthfi M. Ali Fauzi Muhammad Aminul Akbar Muhammad Hafiz Azhar Muhammad Tanzil Furqon Mustika Mentari Nabila Nur’aini Natasya Eldha Oktaviana Novanto Yudistira Nugraha, Dwi Cahya Astria Nuraini, Nabila Putra Pandu Adikara Putra Pandu Adikara Putra Pandu Adikari Raditya Rinandyaswara Randy Cahya Wihandika Ratih Kartika Dewi Renaldi Primaswara Praetya Rifki Akbar Siregar Rizal Maulana, Rizal Rizky Maulana Iqbal Sigit Adinugroho Simangunsong, Bryan Nicholas Josephin Hotlando Siti Mutrofin Tesa Eranti Putri Utaminingrum, Fitri Wijaya Kurniawan Yusuf Gladiensyah Bihanda