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Automated DFU detection through GA-selected CNN ensemble with Grad-CAM interpretability.

Shreya Girotra¹, Achin Jain¹, Sarita Yadav¹

¹Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.

Scientific Reports

|December 14, 2025

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This summary is machine-generated.

This study introduces an optimized deep learning model for detecting diabetic foot ulcers (DFU). The model achieved 97% accuracy, improving detection and interpretability for better patient outcomes.

Area of Science:

Medical image analysis
Artificial intelligence in healthcare
Diabetic complications

Background:

Diabetic foot ulcers (DFU) are a severe complication of diabetes, often leading to amputation and significant mortality.
Current deep learning models for DFU detection face challenges in optimizing accuracy and interpretability.
Advancements in machine learning offer potential for improved DFU detection through medical image analysis.

Purpose of the Study:

To develop and validate an optimized deep learning model for accurate and interpretable detection of diabetic foot ulcers (DFU).
To enhance the performance of DFU detection models by integrating Convolutional Neural Network (CNN) structures with Genetic Algorithm (GA) ensembles.
To improve the transparency of DFU detection models using visualization techniques like Grad-CAM.

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Main Methods:

Developed a custom Convolutional Neural Network (CNN) architecture trained with seven different optimizers.
Utilized a Genetic Algorithm (GA) to create an ensemble model from the best-performing individual CNN models.
Implemented Grad-CAM (Gradient-weighted Class Activation Mapping) for model interpretability and decision transparency.

Main Results:

The GA-based ensemble model achieved high performance metrics: 97% accuracy, 95% precision, 99% recall, and 97% F1 score.
The ensemble model outperformed individual single models in DFU detection accuracy and robustness.
Grad-CAM successfully visualized ulcer-associated features, providing insights into the model's decision-making process.

Conclusions:

The proposed optimized deep learning model, combining CNNs and GA ensembles, significantly improves DFU detection accuracy and interpretability.
Grad-CAM enhances model transparency, offering valuable insights for healthcare professionals in clinical decision-making.
This approach represents a promising advancement in leveraging AI for managing diabetic foot complications.