Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/55761
Title: An efficient 3D color-texture feature and neural network technique for melanoma detection
Authors: Warsi, Firoz
Khanam, Ruqaiya
Kamya, Suraj
Suárez Araujo, Carmen Paz 
UNESCO Clasification: 120304 Inteligencia artificial
32 Ciencias médicas
220990 Tratamiento digital. Imágenes
Keywords: Melanoma
Color texturefeature
Dermoscopic image
Neural network classifier and skin cancer
Issue Date: 2019
Journal: Informatics in Medicine Unlocked 
Abstract: 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 PH 2 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.
URI: http://hdl.handle.net/10553/55761
ISSN: 2352-9148
DOI: 10.1016/j.imu.2019.100176
Source: Informatics in Medicine Unlocked [ISSN 2352-9148], v. 17 (2019), 100176
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