Enhancing the Precision of Skin Cancer Diagnosis by Leveraging Advanced Deep Learning Techniques Integrated with Optimization Strategies

Abstract

Melanoma, a particularly aggressive variant of skin cancer, continues to represent a significant global health concern due to its high incidence and mortality rates. Early and precise diagnosis is vital for improving patient outcomes and enabling timely therapeutic intervention. This study introduces a sophisticated diagnostic architecture that synergizes deep learning methodologies with optimization algorithms to bolster the accuracy of skin cancer classification. Specifically, we implement convolutional neural networks (CNNs) augmented by systematic hyperparameter optimization utilizing evolutionary computation and related techniques. The proposed model is trained and evaluated using the HAM10000 dataset, which contains a diverse array of dermatoscopic images. Our empirical findings reveal that the integrated framework outperforms conventional CNN-based approaches, yielding notable enhancements in accuracy, sensitivity, and specificity. These results underscore the efficacy of combining deep learning with optimization strategies to advance the early detection and reliable classification of skin cancer.