Advanced Skin Lesion Classification Using EfficientNetV2-S with a Proposed Segmentation and Hybrid CNN-Transformer Framework

Skin lesion analysis from dermoscopic images has become an important area of computer-aided medical research. Although deep learning models can learn useful visual patterns from these images, reliable classification remains difficult because different lesions can have similar appearances and clinically important classes may be underrepresented. Another challenge is that many image-classification systems process the complete image without explicitly separating the lesion from surrounding skin, hair, and other visual artefacts. In addition, predictions from complex models can be difficult to interpret. This study investigates an EfficientNetV2-S based approach for classifying eight categories of skin lesions and, separately, proposes a segmentation-guided hybrid framework that combines convolutional and transformer-based representations. The verified classifier was evaluated on 120 images and achieved an accuracy of 61.67%, balanced accuracy of 55.47%, macro-F1 of 53.28%, and macro ROC-AUC of 94.85%. The results were not uniform across the eight classes. Dermatofibroma and benign keratosis showed strong recall, while basal cell carcinoma and squamous cell carcinoma remained difficult to identify. The overall sensitivity for the malignant group was 37.5%, demonstrating that the verified classifier is not suitable for clinical diagnosis in its current form. To address these limitations, the proposed framework places lesion segmentation before classification and combines EfficientNetV2 features, which capture local visual patterns, with Vision Transformer features that provide broader contextual information. Grad-CAM is incorporated to provide visual explanations of model predictions. The proposed framework is presented as a research direction rather than as a validated clinical system, since controlled experiments comparing its individual components have not yet been completed. The study therefore establishes a verified baseline while defining a reproducible experimental pathway for evaluating segmentation, feature fusion, loss functions, test-time augmentation, and explainability in future work.