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All Journal JAIS (Journal of Applied Intelligent System) Indonesian Applied Physics Letters
Widayani, Aisyah
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Chemical Engineering, Chemistry & Bioengineering Electrical & Electronics Engineering Materials Science & Nanotechnology Physics

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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. 1 (2024): June 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.
Co-Authors Adi, Dea Zalfa Cahyla Hyperastuty, Agoes Santika Maghriebi, Agiel Ridlo Pradana, Dio Alif Putra, Ayub Manggala Putra, Fadli Dwi Ridho, Moh. Hilmy Faishal Yanuar Mukhammad