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Nurul Fitriah, Nurul
Bidang Pelayanan Medik Rumah Sakit Dr. Ernaldi Bahar Provinsi Sumatera Selatan
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Classification of Foot Wound Severity in Type 2 Diabetes Mellitus Patients Using MobileNetV2-Based Convolutional Neural Network Fitriah, Nurul; Sriani, Sriani
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11015

Abstract

Diabetic Foot Ulcer (DFU) is a serious complication in Type 2 Diabetes Mellitus patients that may lead to amputation if not properly treated. This study employs the MobileNetV2 architecture based on Convolutional Neural Network (CNN) to classify DFU severity into two categories: severe and non-severe. The dataset consists of 1,000 images, divided into 70% training, 20% validation, and 10% testing. Data preprocessing was performed using normalization, augmentation (rotation, flipping, zooming), and dataset balancing to enhance model generalization. The model was trained for 10 epochs with a batch size of 32, learning rate of 0.001, and Adam optimizer. Experimental results show 98% accuracy on validation data with an average precision, recall, and F1-score of 0.98. On the testing stage, the model achieved 94% accuracy with an average precision, recall, and F1-score of 0.94. The confusion matrix also indicates strong performance in distinguishing both classes. This study demonstrates that MobileNetV2-based CNN with proper preprocessing and hyperparameter settings can serve as an effective supporting method for early DFU severity classification, thereby improving the speed and accuracy of medical decision-making.
Co-Authors Ahmad Syai Anggeraini, Hani Aras, Dzulqarnain Aulia, Ikrimah Budiawan, Mahfudin ELFARISNA ELFARISNA Erlina Rahmayuni, Erlina Fadollah, Idawati Fahreza, Muhammad Daffa Fitri, Aida Hafiz, Abdul Harahap, Firda Ramadani Herman, Welly Jayardi, Apris Sural Kongoasa, Andi Muhammad Yusuf Kurniati Kurniati Muhajir Muhajir, Muhajir Muhibbusaabry, Muhibbusaabry Rafa Hidayat, Muhammad Yusuf Rafsanjani H, Dhai Rahma Novita, Dea Sukma, Elsa Mauliani Zaenudin Zaenudin