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All Journal JOURNAL OF HEALTHCARE TECHNOLOGY AND MEDICINE CARING (Center of Publication in Midwifery and Nursing) : Jurnal Publikasi Penelitian Kebidanan dan Keperawatan Indonesian Journal of Obstetrics and Gynecology (Majalah Obstetri dan Ginekologi Indonesia) JOMIS (Journal of Midwifery Science) JURNAL MUTIARA KESEHATAN MASYARAKAT Al-Tamimi Kesmas: Jurnal Ilmu Kesehatan Masyarakat (Journal of Public Health Sciences) Jurnal Maternitas Kebidanan Community Engagement and Emergence Journal (CEEJ) Indonesia Berdaya Colostrum Jurnal Kebidanan Journal of Applied Data Sciences JIDAN (JURNAL ILMIAH KEBIDANAN) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat JIKA Midwifery Journal
Herlina, Sara
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A Hybrid YOLO–CNN Model for Automatic Detection and Severity Assessment of Atopic Dermatitis in Infant Images Setiawan, Debi; Putri, Ramalia Noratama; Herlina, Sara; Hidayanto, Achmad Nizar; Irawan, Yuda; Hohashi, Naohiro
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1212

Abstract

Atopic dermatitis is one of the most common skin diseases affecting infants and children worldwide and has a particularly high prevalence in tropical countries. Traditional diagnosis methods, which still rely on physical examinations and laboratory tests often face challenges such as delays, high costs, and limited facilities, thereby necessitating an artificial intelligence–based system that is more efficient and accurate. This study aims to develop a hybrid YOLO–CNN model for the automatic detection and severity classification of atopic dermatitis in infants. The dataset comprises 2,000 infant skin images, including lesions categorized as mild, moderate, and severe, obtained from an online repository and field observations conducted in three villages. The labeling process was performed by a specialist doctor to ensure clinical validity. In the first step, YOLO was used to detect the lesion area in real time by generating a bounding box. This produced a region of interest (ROI), which was subsequently analyzed by a CNN model employing transfer learning in the second step to determine the severity level. Experimental results indicate that YOLO achieved high detection performance, with an mAP@0.5 of 91.2% and an F1-score of 90.2%, while the CNN model attained an average accuracy of 85% and a macro-F1 score of 85% in classification. The visualization of predictions indicates that most lesions were detected with confidence levels ≥0.9, confirming the model’s consistency. These findings highlight the potential of the hybrid YOLO–CNN framework as a supportive system for digital clinical diagnosis, applicable to both mobile applications and teledermatology services, particularly in regions with limited medical personnel. Future research should employ larger, multi-center datasets and integrate explainable AI approaches to promote broader clinical adoption.
Co-Authors - Darmadi Achmad Nizar Hidayanto Ahmad Jaelani Amir Syamsuadi Ari Diansyah Ayuningtias, Ratih Debi Setiawan, Debi Fitri, Imelda Fridons, Aurelia Hohashi, Naohiro Hotmauli, Hotmauli Islami, Deri Juwita, Sellia Lestiyani, Nurul liza, Nurma Margi Sidoretno, Wahyu Maryanti, Adelina Nasution, Itto Nesyia Neni Widaningsih Nurmaliza, Nurmaliza Putri Wulandini. S Ramalia Noratama Putri Ratih, Rini Hariani Relida, Nova Sakinah, Sri Dewi Siagian, Dewi Sartika Sidoretno, Wahyu Margi Siti Qomariah Siti Qomariah, Siti Sonia, Selvi Utami, Riski Dwi Wiwi Sartika Wiwi Sartika Wulandini, Putri Yose Rizal Yuda Irawan