Combination of CNN and SMOTE-IPF for Early Detection of Diabetes Patients in Thermogram Images - W Mega Adhi Agam Pradhana; Gede Angga Pradipta; Roy Rudolf Huizen (Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha, 2025)
This study aims to enhance the early detection of diabetic complications through the analysis of plantar foot thermogram images using deep learning techniques. A total of 334 thermographic images were utilized, comprising 244 images from 122 diabetic patients (DM class) and 90 images from 45 non-diabetic individuals (control group, CG class). To address the dataset’s imbalance (ratio of 2.64), the Synthetic Minority Over-sampling Technique with Iterative-Partitioning Filter (SMOTE-IPF) was applied both before and after feature extraction. Image quality was further enhanced using Adaptive Histogram Equalization (AHE) and Gamma Correction preprocessing techniques. A Convolutional Neural Network (CNN) model was trained and evaluated on an independent test set of 54 images. The model achieved outstanding results: 99.37% accuracy, 99.37% precision, 100% recall, and a 99.68% F1-score for AHE-processed images. Gamma-corrected images achieved 98.50% accuracy, while original images reached 97.20%. These findings demonstrate the combined value of data balancing and preprocessing in improving non-invasive diabetic foot ulcer detection, offering a promising diagnostic aid for clinical settings.