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All Journal Informatik : Jurnal Ilmu Komputer Building of Informatics, Technology and Science Informatika BERNAS: Jurnal Pengabdian Kepada Masyarakat International Journal of Computer and Information System (IJCIS) Jurnal Abdimas ADPI Sains dan Teknologi Journal of Science and Technology Journal of Student Development Information System (JoSDIS) SAGA: Journal of Technology and Information Systems Edu Komputika Journal
Fithratul Zalmi, Wahyuni
Universitas Sam Ratulangi, Manado
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Journal : building of informatics technology and science

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Fusi Cross-Attention CNN–Transformer untuk Klasifikasi Multi-Kelas Acute Lymphoblastic Leukemia Wahyuni Fithratul Zalmi; Rahmi Putri Kurnia; Yulia Jihan Sy
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9831

Abstract

Acute Lymphoblastic Leukemia (ALL) is a hematological malignancy that requires a quick and accurate initial examination. Peripheral Blood Smear (PBS) imaging can be used as a source of cell morphology information, but image-based classification still faces challenges due to variations in the shape, color, and structure of blood cells. This study proposes an ALL multi-class classification model based on CNN–Transformer cross-attention fusion with two image inputs, namely the original PBS image and the segmented image that is already available in the dataset. The main contribution of this study lies in the integration of the local features of the CNN and the global features of the Transformer through the cross-attention mechanism, as well as the evaluation of the model components through baseline comparison and ablation studies. The dataset used consisted of 3,256 pairs of PBS images in four classes, namely Benign, Early, Pre, and Pro. The test results showed that the model obtained an accuracy of 0.9980 and a macro F1-score of 0.9975 on the test data. Nonetheless, this very high performance needs to be interpreted with caution as the research has not involved external validation based on different institutions or direct assessments by pathologists. Therefore, the proposed model is more appropriately positioned as a potential computational approach to the classification of PBS images, rather than as a final clinical diagnostic system. Advanced evaluation of external datasets, patient-based allocation schemes, and expert validation are required to assess the generalization and clinical relevance of the model.
Co-Authors AA Sudharmawan, AA Ade Yusupa Bernad J. D. Sitompul David Pang Djesmedi, Dinda Fitri Firdalius Hari Saputro, Pujo kurnia, rahmi putri Leatemia, Kevin Mahendra Kusuma Nugraha Pujo Hari Saputro Rahmi Putri Kurnia Reinhard Komansilan Rendy Syahputra Sastra Wandi Nduru Sedihati Kayan Lumbangaol Sitanggang, Jonathan SY, Yulia Jihan Victor Tarigan Yulia Jihan Sy