Deep Learning-Based Diagnosis for Skin Disease Prediction

Abstract

Skin diseases are increasingly prevalent, and their accurate diagnosis remains challenging due to the complexity of medical imaging and the diversity of conditions. Existing systems primarily rely on Generative Adversarial Networks (GANs) for skin disease prediction, but they face stability issues and inconsistencies during training. Additionally, the reliance on synthetic data generation often results in less accurate predictions when the generated images fail to fully capture real-world variability. To overcome these limitations, the proposed system employs EfficientNetB0, a deep learning model optimized for medical image analysis. EfficientNetB0 uses a compound scaling method to balance depth, width, and resolution, enabling efficient feature extraction while maintaining high accuracy. Its lightweight architecture allows for faster processing without compromising performance, making it ideal for skin disease classification. By leveraging EfficientNetB0, the system enhances early detection, improves diagnostic precision, and reduces misclassification risks, ultimately supporting better patient outcomes in clinical practice.

Introduction

Skin diseases vary from benign conditions to malignant cancers, making accurate diagnosis essential. Certain benign lesions like dermatofibroma, nevus, or seborrheic keratosis can visually resemble malignant ones like basal cell carcinoma (BCC), squamous cell carcinoma (SCC), or melanoma, complicating diagnosis. Given these similarities, deep learning models—particularly EfficientNetB0—are used to distinguish between them accurately.

EfficientNetB0 Overview

EfficientNetB0 is a deep learning architecture known for high accuracy with low computational cost. It uses:

• Compound Scaling: Balances depth, width, and resolution efficiently.

• MBConv Layers: Improve feature extraction with fewer parameters.

• Squeeze-and-Excitation Blocks: Focus the model on the most important features.

• Swish Activation & Downsampling: Enhance learning and efficiency.

Due to its lightweight design and accuracy, EfficientNetB0 is well-suited for medical image analysis, including mobile or resource-limited applications.

Literature Insights

Multiple studies confirm the effectiveness of AI, particularly CNNs and models like EfficientNetB0, in skin disease detection. Key points:

• Traditional diagnosis is subjective and error-prone.

• CNNs outperform older ML models like SVM and KNN in skin lesion classification.

• AI models reduce diagnostic errors and support early treatment.

• Feature extraction techniques like GLCM, WLD, and wavelet transform improve image analysis.

Proposed System Workflow

1. Data Collection: Images sourced from Kaggle; cleaned and balanced.

2. Pre-processing: ROI extraction, grayscale conversion, noise removal, contrast enhancement.

3. Feature Extraction: Includes edge detection, histogram analysis, and wavelet transform.

4. Model Training: EfficientNetB0 trained on labeled data using batch normalization and dropout.

5. Testing & Prediction: Uses unseen data for accuracy evaluation (metrics: accuracy, precision, F1-score).

6. Stem Layer: Initial convolution + normalization + Swish activation + downsampling.

7. MBConv Blocks: Core processing units that maintain detail while reducing computation.

Advantages & Applications

• High Accuracy in distinguishing between visually similar lesions.

• Lightweight Architecture makes it suitable for real-time or mobile deployment.

• Supports Early Diagnosis, especially in remote or underserved regions.

• Scalable & Generalizable, improving dermatological care globally.

Conclusion

In conclusion, The proposed system enhances the accuracy of skin disease diagnosis by integrating EfficientNetB0, a deep learning model designed for efficient medical image analysis. Traditional methods, such as Generative Adversarial Networks (GANs), often struggle with stability and inconsistencies, particularly when generating synthetic data that may not fully capture real-world variations. This can lead to misclassification and unreliable predictions. To overcome these issues, EfficientNetB0 employs a compound scaling method that optimally balances network depth, width, and resolution, ensuring effective feature extraction while maintaining computational efficiency. One of the key advantages of EfficientNetB0 is its lightweight architecture, which allows for faster processing without sacrificing accuracy. This makes it highly suitable for real-time clinical applications where prompt and precise diagnosis is critical. By leveraging this model, the proposed system improves early detection, enhances diagnostic precision, and reduces the likelihood of errors, ultimately leading to better patient outcomes. Furthermore, its scalability ensures that the system can be adapted to various medical imaging tasks beyond skin disease classification. With its superior performance in feature learning and classification, this approach offers a more reliable, efficient, and practical solution for automated skin disease detection, supporting medical professionals in delivering accurate and timely diagnoses. Future work can focus on expanding the dataset to include a wider range of skin conditions for improved generalization. Enhancing the model with attention mechanisms or hybrid architectures could further boost accuracy. Integration with mobile applications can enable real-time diagnosis for remote healthcare. Additionally, explainable AI techniques can be explored to provide better interpretability of predictions, aiding dermatologists in clinical decision-making. Lastly, incorporating multimodal data such as patient history and symptoms could further refine diagnostic precision.

References

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Copyright

Copyright © 2025 Dr. R. G. Suresh Kumar, S Priyadharsheni, B Nandhini, B Keerthanaa, M Aruna Devi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.