Kernel sparse representation based model for skin lesions segmentation and classification

BACKGROUND AND OBJECTIVES

Melanoma is a dangerous kind of skin disease with a high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of melanoma in a primary phase can be helpful for its cure. Due to costs for dermatology, we need an automatic system to diagnose melanoma through lesion images.

METHODS

Here, we propose a sparse representation based method for segmentation and classification of lesion images. The main idea of our framework is based on a kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space. Our novel formulation for discriminative kernel sparse coding jointly learns a kernel-based dictionary and a linear classifier. We also present an adaptive K-SVD algorithm for kernel dictionary and classifier learning.

RESULTS

We test our approach for both segmentation and classification tasks. The evaluation results on both dermoscopic and digital datasets demonstrate our approach to be competitive as compared to the available state-of-the-art methods, with the advantage of not needing any pre-processing.

CONCLUSIONS

Our method is insensitive to noise and image conditions and can be used effectively for challenging skin lesions. Our approach is so extensive to be adapted to various medical image segmentations.

Meta modelling of job satisfaction effective factors for improvement policy making in organizations

Purpose

– The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making.

Design/methodology/approach

– Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies.

Findings

– On the basis of these empirical findings, the authors first aim to identify relationships between LMX, task demand, social structure and individual factors, organizational factors, job properties, which are easier to be employed in strategy formulation for job satisfaction, and then determine the sub-factors and subsequently cluster them. The effectiveness of the proposed model is verified by a case study.

Originality/value

– Here, a Meta modeling based on regression, neural network, and clustering is proposed to analyze the job satisfaction factors and improvement policy making.