Healthcare Innovation for Skin Disease Analysis Using ML and DL

Abstract

Skindiseases represent a significant health- care burden worldwide, and rapid diagnosis is essential foreffectivetreatment.Machinelearning(ML)and deep learning (DL) are increasingly applied to automate and improve dermatological diagnosis. In this study, we present a novel skin disease classification pipeline that integrates multiple ML and DL techniques in a unified framework. First, dermoscopic images are preprocessed toremoveartifacts (e.g.,hair)usingmorphologicalfilters and inpainting, and segmented using clustering and GrabCut algorithms to isolate the lesion region. To augment the training data and address class imbalance, wetrainagenerativeadversarialnetwork (GAN)onamulticlassskindataset.Therefineddatasetisthen fed into dual deep feature extractors (ResNet50 and DenseNet121, pretrained on ImageNet) to obtain rich feature representations. These features are combined through an attention-based fusion strategy and passed to a hybrid classifier comprising gradient boosting models and a support vector machine. Key contributions include the pipeline of artifact removal and segmentation, GAN- based data enhancement, and an ensemble classifica-tion strategy with explainable-AI components for trans- parency.Weevaluatetheapproachonapubliclyavailable multi-categoryskindiseasedataset(21classes),achieving high diagnostic accuracy (>90%) on the test set. Results indicate that our integrated ML/DL system outperforms traditional methods, reduces time to diagnosis, and supports efficient teledermatology for enhanced patient care. Moreover, this approach sets a foundation for real- world deployment in healthcare environments by ensur- ing model interpretability and scalability. The success of our framework demonstrates the transformative impact AIcanhaveonimprovingdermatologicaldiagnosticsand patient outcomes, especially in underserved and remote areas where access to specialists is limited.

Introduction

Skin diseases like melanoma, eczema, and psoriasis affect many people and require early diagnosis for effective treatment. Traditional diagnosis depends on specialists who are often scarce, causing delays. AI, particularly machine learning (ML) and deep learning (DL), shows promise in automating skin lesion classification through image analysis.

Recent research highlights that advanced ML/DL techniques, such as convolutional neural networks (CNNs), ensemble models, and hybrid approaches, improve diagnostic accuracy. However, challenges remain, including artifact interference in dermoscopic images, imbalanced datasets favoring common diseases, lack of explainability in AI models, and difficulty generalizing across diverse skin types and conditions.

To address these, the authors propose a multi-stage framework combining:

• Advanced preprocessing to remove artifacts (hair, noise, uneven lighting) using black-hat filtering and inpainting.

• Precise lesion segmentation with k-means clustering and GrabCut.

• Data augmentation via generative adversarial networks (GANs) to create synthetic images, especially for underrepresented classes.

• Deep feature extraction from two pretrained CNNs (ResNet50 and DenseNet121) fused with attention mechanisms.

• Hybrid classification using ensemble methods (SVM, Random Forest, XGBoost, LightGBM) combined in a stacked meta-classifier.

• Explainability through Grad-CAM saliency maps to highlight important image regions for model decisions.

Using a large public dermoscopic dataset, the proposed system achieved a high classification accuracy of 92.3%, outperforming individual models. The explainability component improved clinical trust by showing the model focused on relevant lesion areas.

Limitations include occasional misclassification of rare diseases, limited fine-grained interpretability of explanations, potential demographic bias, and practical deployment concerns like model size for telemedicine. Future work aims to enhance interpretability, include diverse patient metadata, and optimize models for mobile use.

Conclusion

We proposed a novel integrated ML/DL framework for automated skin disease diagnosis, combining ad- vanced artifact removal, lesion segmentation, GAN- based data augmentation, dual CNN feature extraction with attention fusion, and hybrid ensemble classifi- cation. Extensive evaluation shows that our system achieves state-of-the-art diagnostic accuracy (>92%) across multiple skin diseases. Explainable AI tech- niques(Grad-CAM)confirmmodeltransparency,criti- cal for clinical adoption. This research demonstrates that combining prepro- cessing, data augmentation, diverse feature extraction, and ensemble strategies can substantially improve der- matologicalAIsystems.Theproposedpipelineholds promise for real-world teledermatology applications, providing accessible and accurate skin disease diagno- sistounderservedpopulations.Futureworkwillextend thisapproachtobroaderdatasets,incorporatemetadata, and optimize the system for deployment on mobile and edge devices

References

[1] Ahammed, S., et al. (2020). \"Skin lesion classification us-ing machine learning with preprocessing.\" Biomedical Signal Processing and Control, 57, 101819. [2] Sun,X.,etal.(2021).\"Machinelearninganddeeplearningfor skin disease classification: A review.\" Journal of Healthcare Engineering, 2021, 1-19. [3] Johnson,K.W.,etal.(2022).\"Comparingdeeplearning to traditional machine learning for skin lesion diagnosis.\" Artificial Intelligence in Medicine, 124, 102184. [4] Bandyopadhyay, S., & Dutta, S. (2018). \"Fusion of CNN fea- turesforskindiseaseclassification.\"InternationalConference on Computer Vision Systems. [5] Kalaivani, P., & Karpagavalli, S. (2020). \"Hybrid deep learn- ing approach for skin lesion classification.\" Materials Today: Proceedings, 33, 4863-4870. [6] AlDera, R., & Othman, A. (2018). \"A hybrid approach for skin disease detection using image processing and machine learning.\" International Journal of Advanced Computer Sci- ence and Applications, 9(12), 146-151

Copyright

Copyright © 2025 Dhruv Malhotra, Dhruv Kuchhal. 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.