Development of A Clinically-Oriented Expert System for Differentiating Melanocytic from Non-melanocytic Skin Lesions

Abstract

Differentiating melanocytic from non-melanocytic (MnM) skin lesions is the first and important step required by clinical experts to automatically diagnosis pigmented skin lesions (PSLs). In this paper, a new clinically-oriented expert system (COE-Deep) is presented for automatic classification of MnM skin lesions through deep-learning algorithms without focusing on pre- or post-processing steps. For the development of COE-Deep system, the convolutional neural network (CNN) model is employed to extract the prominent features from region-of-interest (ROI) skin images. Afterward, these features are further purified through stack-based autoencoders (SAE) and classified by a softmax linear classifier into categories of melanocytic and non-melanocytic skin lesions. The performance of COE-Deep system is evaluated based on 5200 clinical images dataset obtained from different public and private resources. The significance of COE-Deep system is statistical measured in terms of sensitivity (SE), specificity (SP), accuracy (ACC) and area under the receiver operating curve (AUC) based on 10-fold cross validation test. On average, the 90% of SE, 93% of SP, 91.5% of ACC and 0.92 of AUC values are obtained. It noticed that the results of the COE-Deep system are statistically significant. These experimental results indicate that the proposed COE-Deep system is better than state-of-the-art systems. Hence, the COE-Deep system is able to assist dermatologists during the screening process of skin cancer.

Authors and Affiliations

Qaisar Abbas

Keywords

Related Articles

An Aspect Oriented Programming Framework to Support Transparent Runtime Monitoring of Applications

Monitoring the runtime state and behavior of applications is very important to evaluate the performance of these applications and to inspect their behavior. In case of legacy applications that have been developed without...

Segmentation of Ultrasound Breast Images using Vector Neighborhood with Vector Sequencing on KMCG and augmented KMCG algorithms

B mode ultrasound (US) imaging is popular and important modality to examine the range of clinical problems and also used as complimentary to the mammogram imaging to detect and diagnose the nature breast tumor. To unders...

An Algorithm that Prevents SPAM Attacks using Blockchain

There are many systems and methods for prevent-ing spam attacks. However, at present there is no specific tried-and-true method for preventing such attacks. In this paper, we propose an algorithm, “SAGA BC” to prevent sp...

scaleBF: A High Scalable Membership Filter using 3D Bloom Filter

Bloom Filter is extensively deployed data structure in various applications and research domain since its inception. Bloom Filter is able to reduce the space consumption in an order of magnitude. Thus, Bloom Filter is us...

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, devel...

Download PDF file
  • EP ID EP259981
  • DOI 10.14569/IJACSA.2017.080704
  • Views 93
  • Downloads 0

How To Cite

Qaisar Abbas (2017). Development of A Clinically-Oriented Expert System for Differentiating Melanocytic from Non-melanocytic Skin Lesions. International Journal of Advanced Computer Science & Applications, 8(7), 24-29. https://europub.co.uk/articles/-A-259981