Paper Title
A HYBRID CNN-BASED MODEL FOR RELIABLE AND TRANSPARENT SKIN CANCER DETECTION

Abstract
Skin cancer is a big issues with health nearby worldwide, and boththe melanoma and non-melanoma forms take illness and death rates go up. Standard diagnostic techniques such as clinicalobservation, dermoscopic testing, and subsequent. Histopathological tests often prove subjective, arduous, and reliant on expertise. Recent advances in computational imaging have opened up new possibilities for automated and objective diagnosis. In this context, deep learning models, specifically those based on Convolutional Neural Network (CNN) architectures, have shown great success of identifying subtle visual features in dermoscopic images. The present research offers an advanced deep learning structure to facilitate automatic detection and classification ofskin cancer utilizing high-resolution dermoscopic images. The suggested approach includes a better preprocessing pipeline that incorporates improving the lighting, balancing the colors, while earning free of hair artifacts to make the spots easier to see. Transfer learning is used to optimize modern CNN architectures like ResNet50, DenseNet121, and InceptionV3. Then, adaptive optimization is utilized for effectively obtaining spatial and structural lesion patterns. using information augmentation and cross- validation by k-fold in tandem implies a precedent attainable and reliable with regard to statistical. When compared with established machine learning methods, experimental tests on benchmark datasets such asISICand HAM10000 demonstrate that this approach is better interms of accuracy, sensitivity, specificity, and ROC-AUC. furthermore, visualization techniques like Gradient-weighted Class Activation Mapping (Grad-CAM) works to discover and confirm the model's decisions, thus enhancing its clinical transparency. The results show that deep learning can greatly improve the accuracy and speed of skin cancer screening, makingitausefulandobtainabletoolfordermatologicalsurgeons.Futureeffortsshallfocusonintegratingof multimodal information, and including patient demographics and genetic information, inorderto construct a more comprehensive and specific diagnostic framework.