Efficient Detection of Skin Cancer Using Deep Learning Techniques and a Comparative Analysis Study
Source of Publication
Lecture Notes in Electrical Engineering
Many skin lesions may result in the wrong diagnosis of skin cancer, leading to delays and ultimately making the cure impossible. Framed within this statement, this article proposes an efficient skin cancer detection model and compares the six pre-trained models, used for transfer learning in ISIC 2019 dataset. Three most common types of skin cancer—melanoma, nevus, and basal cell carcinoma—are classified by using the transfer learning on the pre-trained models of the ISIC 2019 dataset, to conclude the most accurate detection results with training and test accuracy of 99.73% and 93.79%, respectively.
Springer Nature Singapore
Skin cancer detection, Deep convolutional neural network, Pre-trained models, Transfer learning
Hashim, Mehtab; Khattak, Asad Masood; and Taj, Imran, "Efficient Detection of Skin Cancer Using Deep Learning Techniques and a Comparative Analysis Study" (2023). All Works. 5876.
Indexed in Scopus