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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal ABDINUS : Jurnal Pengabdian Nusantara JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF)
Widhiyasana, Yudi
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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A Morphology Processing Approach For Image Processing In Cancer Diagnosis Hutahaean, Jonner; Widhiyasana, Yudi; Ramdhani, Algi Fari
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4783

Abstract

Early tumor detection is critical for improving cancer treatment outcomes, enabling less invasive and more cost-effective interventions. However, limited access to pathologists and high patient volumes reduce diagnostic efficiency, particularly in underserved regions, underscoring the urgency for computational support tools. While deep learning has shown promise in tumor detection, it requires extensive annotated datasets, high computational resources, and long processing times, making it less feasible in certain contexts.This study introduces a lightweight image processing approach for detecting tumors in Hematoxylin and Eosin (H&E)–stained histopathology images without deep learning. Using data from the PAIP 2023 Tumor Cellularity challenge, the proposed method applies histogram equalization, bilateral filtering, morphological transformations, bitwise operations, and an improved algorithm adapted from prior research. The method achieves IoU (Intersection of Union) of 0.93 compared to pathologist-determined ground truth. The results indicate that this approach can serve both as a standalone segmentation tool and as a preprocessing stage for deep learning pipelines, enhancing accessibility, reducing computational costs, and supporting broader adoption of computer-aided pathology in resource-limited settings.
Co-Authors Eddy Bambang Soewono Elfada, Berliana Gardara, Suci Awalia Gelar, Trisna Gemilang, Angga Hakim Firdaus, Lukmannul Jonner Hutahaean Muhammad Rizqi Sholahuddin Priyanto Hidayatullah, Priyanto Rachmat, Setiadi Ramdhani, Algi Fari Sari, Aprianti Nanda Siti Dwi Setiarini Sofy Fitriani Syakrani, Nurjannah