Identifying Melanoma Skin Disease Using Convolutional Neural Network DenseNet-121

Advancements in melanoma detection are imperative for early intervention and improved patient outcomes. This study explores the transformative potential of DenseNet-121 within CNNs for melanoma identification. Leveraging a dataset from the ISIC 2019 challenge, the model achieved a remarkable 90% accuracy, surpassing traditional models. DenseNet-121 demonstrated adaptability across diverse lesion variations, showcasing its superiority. The study's implications extend to real-time applications, collaborative efforts, and ethical considerations, heralding a significant stride in reshaping melanoma diagnostic standards.