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 multicategoryskindiseasedataset(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