Classification of Foot Wound Severity in Type 2 Diabetes Mellitus Patients Using MobileNetV2-Based Convolutional Neural Network
DOI:
https://doi.org/10.30871/jaic.v9i5.11015Keywords:
Convolutional Neural Network (CNN), MobileNetV2, Deep Learning (DL), Image Classification, Saverity of injuryAbstract
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.
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