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A Hybrid CNN Framework DLI-Net for Acne Detection with XAI.

Shaila Sharmin¹, Fahmid Al Farid², Md Jihad³

¹Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh.

Journal of Imaging

|April 25, 2025

Summary

This study presents a novel hybrid deep learning model for accurate acne detection and classification. The AI model achieved 97% accuracy, significantly improving dermatological care and patient outcomes.

Keywords:

DLI-Net acne detection deep learning explainable AI image classification image segmentation

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Acne is a common skin condition affecting well-being.
Accurate acne detection is vital for effective dermatological care.
Deep learning shows promise for improving acne diagnosis speed and accuracy.

Purpose of the Study:

To introduce a novel hybrid deep learning model for acne detection and classification.
To enhance diagnostic accuracy and efficiency in dermatology.
To improve clinical decision-making and patient outcomes through AI.

Main Methods:

Developed a hybrid model combining DeepLabV3 for segmentation and InceptionV3 for classification.
Trained the model on a custom dataset.
Utilized Grad-CAM for visualization to enhance model interpretability.

Main Results:

Achieved exceptional performance with 97% validation and test accuracy.
Obtained an F1 score, precision, and recall of 0.97.
Outperformed existing baseline models in acne detection and classification.

Conclusions:

The hybrid AI model offers a robust and accurate solution for acne detection.
AI has transformative potential in dermatology, improving clinical decisions and patient care.
Enhanced model interpretability through Grad-CAM provides transparent insights.