Paper Title
Advanced Hybrid Multi-Class Skin Cancer Classification Framework with Deep Learning Using Enhanced Segmentation and Feature Extraction Techniques

Abstract
Skin cancer, a growing global health concern, is primarily categorized into melanoma and non-melanoma types. Effective early detection is crucial for improving patient outcomes, as it can significantly increase survival rates. Traditional diagnostic methods, including visual inspection and biopsy, are time-consuming and invasive. Recent advances in deep learning offer promising alternatives by automating and improving the precision of skin cancer classification. This study introduces an advanced deep learning framework designed to improve multi-class skin cancer classification. We propose a hybrid approach integrating several innovative techniques. First, images undergo pre-processing with adaptive median filtering and SMOTE-based data augmentation to enhance quality. Next, the Enhanced Slope Difference Multi-Thresh Distribution (ESDiff-MTD) model is employed for precise segmentation of skin lesions. Feature extraction is performed using the Dilated Group Search Residual Network (DGS-ResNet), which reduces computational overhead. Classification is achieved through the Relative Self-attention Intermittent Convolution Network (RSAtten-ICN), addressing the vanishing gradient problem and minimizing information loss. The proposed model aims to offer a robust solution for skin cancer detection by combining these advanced techniques. Experimental validation will demonstrate the model's performance compared to existing methods, focusing on accuracy, precision, recall, and other metrics. The outcomes are expected to contribute to more effective and efficient skin cancer diagnosis, ultimately supporting early detection and better patient management. Keywords - Deep Learning, CNN, Image Segmentation, Feature Extraction, Adaptive Filtering