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All Journal JAIS (Journal of Applied Intelligent System) Indonesian Applied Physics Letters Brilliance: Research of Artificial Intelligence Jurnal Abdimas Kesehatan Green Health: Journal of Health Sciences, Nursing and Nutrition
Aisyah Widayani
Universitas Airlangga
Chemical Engineering, Chemistry & Bioengineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Other

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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): Indonesian Applied Physics Letters - 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.
Real-time Detection of Magnetic Resonance Imaging (MRI) Safe Label using Deep Learning for Standardization Aisyah Widayani; Adrian Dwi Nugroho; Alifatus Wahyu Nur Ma’rifah
Brilliance: Research of Artificial Intelligence Vol. 6 No. 2 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i2.8453

Abstract

Magnetic Resonance Imaging (MRI) is an imaging modality that uses a non-ionising magnetic field, so it does not cause radiation exposure. However, a high magnetic field strength still poses a significant safety risk due to projectile effects, so screening is necessary to verify that standard MRI objects are present. This study aims to develop an MRI-safe Label Detection using You Only Look Once version 11 (YOLOv11) Framework. The YOLOv11 is one of the Deep Learning algorithms that is used for Real-time Object Detection with Medical Imaging Standardization. The dataset used consists of a standard MRI bed image and a normal patient bed obtained from several MRI facilities, with an image size of 640 pixels, and divided into four classes. The results show that the model achieves an accuracy of 97,5%, a precision of 99,1%, a recall of 98,8%, an F1-Score of 98,9%, and an mAP50 of 98,9%, which indicates excellent detection performance. The Loss curve analysis shows a stable training process without any indication of significant overfitting. In addition, the confusion matrix shows high classification ability in each class. This research aims to develop an automated safety screening system for zone three MRI to reduce the risk of accidents. However, limitations include the small number of datasets and the limited variety of objects. Therefore, further development is recommended by increasing the variety of object data and integrating real-time systems and supporting hardware.
Edukasi Interaktif Tuberculosis dan Keamanan Foto Rontgen untuk Percepatan Program TOSS-TB di Puskesmas Kowel, Kab. Pamekasan Berliana Devianti Putri; Winda Kusumawardani; Tesa Eranti Putri; Riris Medawati; Endah Sekar Palupi; Cendra Devayana Putra; Aisyah Widayani; Alif Majid Firdaus; Andyka Salom
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).
Optimisation of Renal Cyst Detection in Ct Urography Images Using Neo-ZasAI Based on the YOLO Algorithm Zarkasyi Azri Sardar; Sudiyono Sudiyono; Rini Indrati; Aisyah Widayani
Green Health International Journal of Health Sciences Nursing and Nutrition Vol. 3 No. 1 (2026): January: Green Health: Journal of Health Sciences, Nursing and Nutrition
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenhealth.v3i1.268

Abstract

Background: Accurate detection of renal cysts on CT urography requires high diagnostic precision, while manual interpretation by radiologists is susceptible to inter-observer variability and potential delays in clinical decision-making. These challenges underscore the need for a reliable automated detection system to support radiological assessment. Objective: This study aims to develop and evaluate the performance of the Neo-ZasAI application based on the YOLOv8 algorithm for the automatic identification of renal cysts. Methods: Employing a Research and Development design using the ADDIE model, the study encompassed needs analysis, model design, software development, system implementation using 200 CT urography images, and diagnostic performance evaluation. Classification results generated by Neo-ZasAI were compared with radiologist readings through confusion matrix analysis and ROC–AUC assessment. Results: The findings indicate that Neo-ZasAI achieved an accuracy of 97,5%, sensitivity of 96%, specificity of 99%, positive predictive value of 98,9%, and negative predictive value of 96,1%. The ROC analysis yielded an AUC of 0.988 (p < 0.001), demonstrating excellent discriminative capability and high concordance with radiologist interpretations as the diagnostic gold standard. Conclusion: These results suggest that Neo-ZasAI is capable of performing rapid, consistent, and accurate renal cyst detection and is thus feasible for implementation as a clinical decision support system in radiology, with potential integration into PACS workflows and further development to enhance model generalizability.
Co-Authors Adi, Dea Zalfa Cahyla Adrian Dwi Nugroho Alif Majid Firdaus Alifatus Wahyu Nur Ma’rifah Andyka Salom Berliana Devianti Putri Cendra Devayana Putra Endah Sekar Palupi Hyperastuty, Agoes Santika Maghriebi, Agiel Ridlo Medawati, Riris Pradana, Dio Alif Putra, Ayub Manggala Putra, Fadli Dwi Ridho, Moh. Hilmy Faishal Rini Indrati Sudiyono Sudiyono Tesa Eranti Putri Winda Kusumawardani Yanuar Mukhammad Zarkasyi Azri Sardar