Title: Melanoma classification by 3D colour-texture feature and neural network with improved computational complexity using PCA

Authors: Mohd Firoz Warsi; Ruqaiya Khanam; Usha Chauhan; Suraj Kamya

Addresses: Galgotias University, Greater Noida, India ' Faculty of ECE, Sharda University, Greater Noida, India ' Faculty of ECE, Galgotias University, Greater Noida, India ' Lagozon Technologies Private Limited, Noida, India

Abstract: The most severe kind of skin cancer is malignant melanoma. It can grow anywhere on the body. Its exact cause is still unclear but typically it is caused by ultraviolet exposure from sun or tanning beds. Its detection plays a very significant role because if detected early then it is curable, before the spread has begun. In this paper, a computationally improved [using principal component analysis (PCA)] feature extraction method named 3D colour texture feature (CTF) is represented which is well discriminative. For classification of melanoma from dermoscopic images, a comparison of different types of machine-learning classification algorithms is evaluated, out of which back propagation neural network (NN) classifier outperforms all other and produce best results, i.e., accuracy = 98.5%, sensitivity = 99.4%, specificity = 95.0%. Obtained results are even better than benchmarking results of PH² dataset. Comparisons of results with other similar novel works are also discussed.

Keywords: melanoma; colour texture feature; CTF; dermoscopic image; neural network classifier; PCA; PH²; skin cancer.

DOI: 10.1504/IJMEI.2022.123929

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.4, pp.369 - 378

Received: 21 May 2020
Accepted: 11 Oct 2020
Published online: 05 Jul 2022 *

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