Development of A Clinically-Oriented Expert System for Differentiating Melanocytic from Non-melanocytic Skin Lesions
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 7
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
The Effectiveness of D2L System: An Evaluation of Teaching-Learning Process in the Kingdom of Saudi Arabia
High quality education could be achieved through an e-learning system as it increases the educational information accessibility, service availability and accuracy when compared to a conventional face-to-face teaching-lea...
Micro Agent and Neural Network based Model for Data Error Detection in a Real Time Data Stream
In this paper, we present a model for learning and detecting the presence of data type errors in a real time big data stream processing context. The proposed approach is based on a collection of micro-agents. Each micro-...
Arabic Text Categorization using Machine Learning Approaches
Arabic Text categorization is considered one of the severe problems in classification using machine learning algorithms. Achieving high accuracy in Arabic text categorization depends on the preprocessing techniques used...
Developing a Feasible and Maintainable Ontology for Automatic Landscape Design
In general, landscape architecture includes analysis, planning, design, administration and management of natural and artificial. An important aspect is the formation of so-called sustainable landscapes that allow maximum...
Response Prediction for Chronic HCV Genotype 4 Patients to DAAs
Hepatitis C virus (HCV) is a major cause of chronic liver disease, end stage liver disease and liver cancer in Egypt. Genotype 4 is the prevalent genotype in Egypt and has recently spread to Southern Europe particularly...