A <b>Deep Learning-Based Predictive Model for Pattern Recognition and Classification of Cancerous Skin</b>

Detecting cancerous skin lesions early is essential for improving treatment outcomes and saving lives. However, traditional diagnostic methods often depend on expert judgment, which can be time-consuming and sometimes subjective. To address this challenge, our study introduces a deep learning-based predictive model designed to recognize patterns and classify cancerous skin lesions automatically using medical images. The model leverages convolutional neural networks (CNNs) to capture key visual features from dermoscopic images and employs advanced classification layers to distinguish between benign and malignant lesions with high accuracy. The model was developed and validated using a comprehensive and diverse dataset of dermoscopic images retrieved from the online repository. Through systematic fine-tuning, we optimized the network’s performance with a particular focus on accuracy, precision, and recall, aiming to support dermatologists in clinical decision-making. The resulting outputs of this study demonstrates that the proposed model is both robust and effective in detecting various types of skin cancer, including melanoma, while maintaining a low rate of false positives. Compared to existing diagnostic -methods, the model achieved improved outcomes, recording a precision of 0.92, an accuracy of 89.21%, and a recall of 0.91. These results underscore the significant potential of deep learning to enhance early detection, reduce the reliance on invasive procedures, and ultimately contribute to better patient outcomes.