An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

BMC Cancer

Syed Irtaza Haider¹  Abdualziz Altamrah¹  Musaed Alhussein¹  Khursheed Aurangzeb¹  M. Attique Khan²  Muhammad Sharif²  Tallha Akram³  Aamir Shahzad³ 

[1] College of Computer and Information Sciences, King Saud University;Department of Computer Science, COMSATS Institute of Information Technology;Department of Electrical Engineering, COMSATS Institute of Information Technology;

关键词: Image enhancement;    Uniform distribution;    Image fusion;    Multi-level features extraction;    Features fusion;    Features selection;   

DOI  :  10.1186/s12885-018-4465-8

来源: DOAJ

【 摘 要 】

Abstract Background Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. Methods In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. Results The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. Conclusion The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.

【 授权许可】

Unknown