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All Journal JAIS (Journal of Applied Intelligent System) Indonesian Applied Physics Letters Jurnal Abdimas Kesehatan
Widayani, Aisyah
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Chemical Engineering, Chemistry & Bioengineering Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Nursing Physics Public Health

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Review of Application YOLOv8 in Medical Imaging Widayani, Aisyah; Putra, Ayub Manggala; Maghriebi, Agiel Ridlo; Adi, Dea Zalfa Cahyla; Ridho, Moh. Hilmy Faishal
Indonesian Applied Physics Letters Vol. 5 No. 2 (2024): Volume 5 No. 2 – December 2024
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v5i1.57001

Abstract

Deep learning has revolutionized medical imaging analysis, with YOLOv8 emerging as a promising tool forvarious tasks like lesion detection, organ segmentation and disease classification. This review investigates YOLOv8'sapplications across diverse medical imaging modalities (X-Ray, CT-Scan and MRI). We conducted a systematic literaturesearch across databases like Pubmed, ScienceDirect and IEEE to identify relevant studies evaluating YOLOv8'sperformance in medical imaging analysis. YOLOv8 achieved high performance for meningioma and pituitary tumorswith and without data augmentation (precision >0.92, recall >0.90, mAP >0.93). Glioma detection showed lowerperformance but still promising results (precision >0.86, recall >0.81, mAP >0.86). Breast cancer detection with SGDoptimizer yielded best performance with an average mAP of 0.87 for mass detection. The model achieved high accuracyin detecting normal (mAP 0.939) and malignant lesions (mAP 0.911). YOLO v8 on Dental radiograph successfullydetected cavities, impacted teeth, fillings and implants (precision of >0.82, recall of >0.78 and F1-Score of >0.80). Lastly,for lung disease classification, YOLOv8 achieved high accuracy (99.8% training and 90% validation) in classifyingnormal, COVID-19, influenza and lung cancer disease. With the importance to improve clinical decision-making andpatient outcomes in healthcare, the YOLOv8 algorthm underscores the importance of pre-processing, augmentation andoptimization of key hyperparameters.
Pneumonia Detection on X-rays Image using YOLOv8 Model Hyperastuty, Agoes Santika; Pradana, Dio Alif; Widayani, Aisyah; Putra, Fadli Dwi; Mukhammad, Yanuar
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.10865

Abstract

Pneumonia is an acute inflammatory disease of lung tissue. It is usually caused by microorganisms such as bacteria, fungi and viruses. The young children are particularly vulnerable to this illness. Report in 2019 shows that pneumonia kills almost 2,000 children under the age of five every day worldwide and affects over 800,000 children under the age of five annually. Analyzing the chest X-ray results of the patient's body is one method of diagnosing pneumonia. Therefore, this research was done to deploy a deep learning to identify the healthy and pneumonia affected lungs from chest X-ray images in order to aid in the diagnosing process. This research was done by using 2000- chest X-ray dataset—of which 1500 pneumonia lung data and 500 normal lung data. The computer vision model YOLOv8 is used in this study. The accuracy results from the training process were 56.15% in the pneumonia class and 92.03% in the normal class. Wether in the testing process yielded an average value of 0.482 (48, 2%) for the pneumonia class and 0.675 (67,5%) for the normal class. From these results, there are promising possibilities for developing a pneumonia detection system using YOLO in the future.
Edukasi Interaktif Tuberculosis dan Keamanan Foto Rontgen untuk Percepatan Program TOSS-TB di Puskesmas Kowel, Kab. Pamekasan Putri, Berliana Devianti; Kusumawardani, Winda; Putri, Tesa Eranti; Medawati, Riris; Palupi, Endah Sekar; Putra, Cendra Devayana; Widayani, Aisyah; Firdaus, Alif Majid; Salom, Andyka
Jurnal Abdimas Kesehatan (JAK) Vol. 8 No. 1 (2026): Januari
Publisher : Universitas Baiturrahim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36565/jak.v8i1.1046

Abstract

Tuberculosis (TB) can affect people of all ages, from young to old. It also impacts the quality of life of human resources and can become an obstacle to national development. TB can be prevented by optimizing the TOSS-TB (Find, Treat, and Cure) program initiated by the Ministry of Health of the Republic of Indonesia to achieve the TB-Free 2030 target. Based on the 2023 East Java Provincial Health Profile Report, the case detection and treatment success TB rate in Pamekasan is still low, at 80,2%, which is below the national target of 90%. This activity employs an interactive educational approach to raise awareness about TB and the safety of X-ray examinations through digital gamification, then promotes public understanding of TB screenings and recommendations for visiting community health centers. This information and interactive games were designed in two languages, namely Indonesian and Madurese. The community service activity began with the creation of the application that provides information and interactive games. Socialization and hands-on application were conducted in September 2025. Sixty-five participants, including community health center heads, TB program managers, health cadres, TB patients, and community members living near TB patients, participated in this activity. Results of the Wilcoxon signed-rank test (α=0.05) showed a significant increase in participants' knowledge regarding TB (p=0.000) and knowledge regarding X-ray safety (p=0.000). Participants also experienced improved skills in operating the application as an educational tool for health cadres in the Kowel Community Health Center, Pamekasan. This activity supports the Sustainable Development Goals, specifically SDGs No. 3 (Good Health and Well-being) and SDGs No. 4 (Quality Education).
Co-Authors Adi, Dea Zalfa Cahyla Berliana Devianti Putri Firdaus, Alif Majid Hyperastuty, Agoes Santika Maghriebi, Agiel Ridlo Medawati, Riris Palupi, Endah Sekar Pradana, Dio Alif Putra, Ayub Manggala Putra, Cendra Devayana Putra, Fadli Dwi Ridho, Moh. Hilmy Faishal Salom, Andyka Tesa Eranti Putri Winda Kusumawardani Yanuar Mukhammad