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All Journal G-Tech : Jurnal Teknologi Terapan Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Pengabdian Masyarakat Bangsa
Anissa, Thia
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Journal : G-Tech : Jurnal Teknologi Terapan

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ABO-Vision: Automatic Blood Type Detection with YOLOv4-Tiny and Morphological Image Processing Mubarokah, Ita; Anissa, Thia; Irfa'i, Ahmad Khumedillah
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7475

Abstract

Blood is a red-colored fluid in the human body that plays a crucial role in maintaining the immune system. According to the ABO system, blood is classified into four main types: A, AB, B, and O. This classification is essential for facilitating blood transfusions. Currently, blood type determination is still performed manually by healthcare professionals, who observe the presence or absence of clumping (agglutination) in the blood when it reacts with specific antigens.Numerous studies have been conducted to support and enhance healthcare services, particularly as technological advancements continue to grow rapidly across various fields. In the medical field, these advancements have led to the development of increasingly sophisticated medical devices, including blood type detection tools. These devices typically use manual optical sensors to read blood agglutination by detecting changes in light intensity. However, such devices are not fully automated and still require human intervention, making them prone to human error. Today, automated blood type detection systems utilizing cameras and smartphones—integrated with various image processing methods and Artificial Intelligence (AI) are being increasingly developed. Therefore, this study focuses on the development of a blood type detection model that combines image processing and Deep Learning (DL) to support an intelligent, fast, and efficient healthcare system, achieving a detection accuracy of 98%.
Co-Authors Eneng Susilistia Agustini Irfa'i, Ahmad Khumedillah Ita Mubarokah MUBAROKAH, ITA Widodo, Amin