A mobile application LukaKu as a tool for detecting external wounds with artificial intelligence

Intelligence-based medicine Pub Date : 2025-01-01 DOI:10.1016/j.ibmed.2025.100200

Dessy Novita , Herika Hayurani , Eva Krishna Sutedja , Firdaus Ryan Pratomo , Achmad Dino Saputra , Zahra Ramadhanti , Nuryadin Abutani , Muhammad Rafi Triandi , Aldin Mubarok Guferol , Anindya Apriliyanti Pravitasari , Fajar Wira Adikusuma , Atiek Rostika Noviyanti

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Abstract

This study was conducted due to the lack of applications that can assist people intreating common external wounds. Therefore, we proposed the application of image-based detection which takes external wounds and identifies them using Artificial Intelligence namely LukaKu. In addition to detecting the type of wound that occurs, the application is expected to be able to produce first aid and medicine for each existing external wound label. The model used is YOLOv5 with various versions, namely YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. By calculating the validation data, each version has its own precision, recall, f1-score, and Mean Average Precision (mAP) values which are the comparison factors in determining the best model version, where YOLOv5l with mAP value of 0.785 is the best result and YOLOv5n with mAP value of 0.588 is the result with the lowest value. In the model development process, datasets of external injuries are needed to be used during the training process and test datasets for each existing model version. After each version of the model has been successfully built and analysed, the model with the best value is implemented in the mobile application, making it easier for users to access.

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