Evaluating the Performance and Clinical Applications of Multiclass Deep Learning Models for Skin Cancer Pathology Diagnosis (ISIC): A Comparative Analysis of CNN, ViT, and VLM

This study investigates the performance and clinical applications of three cutting-edge deep learning models—Convolutional Neural Networks (CNN) based models: Alexnet VGG19 EfficientNet Resnet GoogleNet, Vision Transformers (VIT) based models: MedVIT SICVIT SEVIT and Visual Language Model (VLM): CLIP—in the diagnosis of skin cancer using the ISIC dataset. Each model represents a different approach to medical image analysis: CNN excels in feature extraction and has been the backbone of medical imaging for years; ViT introduces a novel attention mechanism that enhances the model's ability to capture global context, particularly useful in high-resolution image classification; VLM, specifically the CLIP model, integrate visual and textual data, enabling a multimodal approach that is critical for complex diagnostic tasks. Through a series of experiments, this research evaluates the accuracy, robustness, and generalization capabilities of these models across various datasets of different sizes. The CNN models, while highly accurate, exhibited limitations in handling complex and diverse datasets. ViT models demonstrated superior performance in capturing intricate patterns and maintaining consistency across different image resolutions, though they require significant computational resources. The CLIP model, as a representative VLM, showed unique strengths in combining image data with textual information, improving diagnostic accuracy, particularly in cases where textual context plays a crucial role. The study's findings highlight the potential of these models in clinical settings, where the integration of multimodal data could significantly enhance diagnostic precision and efficiency. However, challenges such as overfitting in smaller datasets and the need for extensive computational resources were identified, suggesting directions for future research to further refine these models and their applications in medical diagnostics.