Deep Learning-Driven Analysis of Skin Lesions for Accurate Melanoma Detection

Deep learning techniques have recently advanced and transformed image analysis, particularly for applications in healthcare. Due to their proficiency in extracting fine features from visual data, these approaches can improve the accuracy of melanoma detection. Deep learning models for skin lesion analysis usually use pooling layers, convolutional processes, and segmentation. Even while these processes help extract features effectively, they could result in a reduction in picture resolution when compared to the original dermoscopic images. This research analyzes dermoscopic pictures of skin cancers and suggests a unique deep learningbased method to overcome these obstacles. After being trained and assessed on benchmark datasets from the ISIC 2018 Challenge, the model achieves a validation accuracy of 96.67%. Its performance surpasses the state-of-the-art methods in classification issues. Additional tests using clinical datasets show how deep learning approaches have a great deal of promise to increase the precision of skin lesion analysis in general and melanoma identification in particular.