Abstract
Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal hyperparameters for the proposed CNN models were determined using the Grid Search Optimization technique. This approach determines optimal values for architectural and fine-tuning hyperparameters, which is essential for learning. Rigorous evaluations of loss, accuracy, and confusion matrix thoroughly assessed the performance of the CNN models. Three datasets from the International Skin Imaging Collaboration (ISIC) Archive were utilized for the classification tasks. The primary objective of this study is to create a robust CNN system that can accurately diagnose skin lesions. Three separate CNN models were developed using the labeled ISIC Archive datasets. These models were designed to accurately detect skin lesions, assess the malignancy of the lesions, and classify the different types of lesions. The results indicate that the proposed CNN models possess robust capabilities in identifying and categorizing skin lesions, aiding healthcare professionals in making prompt and precise diagnostic judgments. This strategy presents an optimistic avenue for enhancing the diagnosis of skin cancer, which could potentially decrease avoidable fatalities and extend the lifespan of people diagnosed with skin cancer. This research enhances the discipline of biomedical image processing for skin lesion identification by utilizing the capabilities of DL algorithms.