Early Detection of Melanoma Disease with Artificial Intelligence-Driven Skin Cancer Diagnosis Using Deep Learning Approach

Melanoma, a type of skin cancer originating in the pigment-producing cells (melanocytes), poses a significant public health concern due to its aggressive nature and potential for metastasis if left untreated. Early detection is pivotal for effective intervention and improved patient outcomes. Traditional methods of diagnosing skin cancer often rely on examinations by dermatologists, which can be subjective and lead to delays in treatment. To address this challenge, the proposed study presents a novel approach utilizing artificial intelligence (AI) powered by deep learning models for the early diagnosis of melanoma, aiming to enhance diagnostic accuracy and facilitate timely medical intervention. The model was trained using the ISIC 2018 skin challenge dataset which includes both malignant and benign skin disease images. The proposed model applied the U-Net algorithm with ResNet50 and VGG16 as Backbone to train the annotated images and Convolutional Neural Network (CNN) to extract intricate features from skin lesion images. The incorporation of Artificial Intelligence does not only speed up the diagnostic process but also reduces subjective variations in assessments. The model was evaluated using various performance metrics to determine the accuracy of the model. The results showed promising outcomes with accuracy rates of 92% for U-Net, 87% for ResNet50, and 88% for VGG16 in early-stage melanoma detection. The developed model serves as a tool for dermatologists by facilitating early detection and intervention of melanoma disease. The research indicates that using AI technology to detect skin cancer has the potential to greatly enhance the accuracy of diagnoses thereby improving outcomes by enabling effective medical interventions.