A new preprocessing approach to improve the performance of CNN-based skin lesion classification

• Published in: Medical & biological engineering & computing Vol. 59; no. 5; pp. 1123 - 1131
• Main Authors: Zanddizari, Hadi, Nguyen, Nam, Zeinali, Behnam, Chang, J. Morris
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021; Springer Nature B.V
• Subjects: Artificial neural networks; Background noise; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classifiers; Computer Applications; Datasets; Disease; Human Physiology; Imaging; Learning algorithms; Lesions; Machine learning; Medical imaging; Melanoma; Neural networks; Original Article; Performance enhancement; Preprocessing; Radiology; Skin diseases; Skin lesions; Training; Skin lesion; Region of interest; Segmentation; Convolutional neural network

Skin lesion is one of the severe diseases which in many cases endanger the lives of patients on a worldwide extent. Early detection of disease in dermoscopy images can significantly increase the survival rate. However, the accurate detection of disease is highly challenging due to the following reas...