Melanoma Skin Cancer Prediction Using VGG19 and DenseNet121

Melanoma skin cancer is one of the most lethal forms of cancer. Deep learning has shown promising results in predicting melanoma skin cancer. This paper proposes a novel method for skin cancer prediction using two modified networks: DenseNet121 and VGG19. In the proposed method, image augmentation is initially performed to increase the diversity of the training data, thereby enhancing the model’s generalization capability. Subsequently, the capabilities of the two networks are enhanced by adding two dense layers with 2560 and 1200 neurons, respectively. The proposed method is applied to the three-class ISIC-2017 dataset, taking into account metrics such as Accuracy, Precision, Recall, and F-measures. The results of the proposed method have shown improvement over many other published works, demonstrating its effectiveness in skin cancer prediction.

Melanoma Skin Cancer Prediction Using VGG19 and DenseNet121 Keywords:

Deep learning , DenseNet , Melanoma skin cancer , VGG19