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
• DOI: 10.1007/s11517-021-02355-5
• ISSN: 0140-0118; 1741-0444

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 reasons: e.g., visual similarity between different classes of disease (e.g., melanoma and non-melanoma lesions), low contrast between lesions and skin, background noise, and artifacts. Machine learning models based on convolutional neural networks (CNN) have been widely used for automatic recognition of lesion diseases with high accuracy in comparison to conventional machine learning methods. In this research, we proposed a new preprocessing technique in order to extract the region of interest (RoI) of skin lesion dataset. We compare the performance of the most state-of-the-art CNN classifiers with two datasets which contain (1) raw, and (2) RoI extracted images. Our experiment results show that training CNN models by RoI extracted dataset can improve the accuracy of the prediction (e.g., InceptionResNetV2, 2.18% improvement). Moreover, it significantly decreases the evaluation (inference) and training time of classifiers as well.