An efficient 3D color-texture feature and neural network technique for melanoma detection

• Published in: Informatics in medicine unlocked Vol. 17; p. 100176
• Main Authors: Warsi, Firoz, Khanam, Ruqaiya, Kamya, Suraj, Suárez-Araujo, Carmen Paz
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2019; Elsevier
• Subjects: Color texturefeature; Dermoscopic image; Melanoma; Neural network classifier and skin cancer; Dermoscopic image; Neural network classifier and skin cancer; Color texturefeature; Melanoma
• ISSN: 2352-9148; 2352-9148
• DOI: 10.1016/j.imu.2019.100176

Malignant melanoma is the deadliest form of skin cancer, but can be more readily treated successfully if detected in its early stages. Due to the increasing incidence of melanoma, research in the field of autonomous melanoma detection has accelerated. In this paper, a new method for feature extraction from dermoscopic images, termed multi-direction 3D color-texture feature (CTF), is proposed, and detection is performed using a back propagation multilayer neural network (NN) classifier. The proposed method is tested on the PH2 dataset (publicly available) in terms of accuracy, sensitivity, and specificity. The extracted combined CTF is fairly discriminative. When it is input and tested in a neural network classifier that is provided, encouraging results are obtained, i.e. accuracy = 97.5%, sensitivity = 98.1% and specificity = 93.84%. Comparative result analyses with other methods are also discussed, and the results are also improved over benchmarking results for the PH2 dataset.