Multispectral skin patterns analysis using fractal methods

Przystalski, Karol; Ogorzalek, M.J.

doi:10.1016/j.eswa.2017.07.011

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Roberta et al [119] proposed a skin lesion diagnosis based on the ensemble model for feature manipulation. Przystalski et al [120] proposed multispectral lesion analysis by fractal methods. Jaisakthi et al [121] proposed a segmentation method for skin lesions using the Grab-Cut algorithm and k-means.…”

Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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“…The Grab-Cut algorithm and k-means were combined in Jaisakthi et al [100] segmentation approach for skin lesions. A technique for melanoma detection using a smartphone was introduced by Do et al [101]. Images were captured with a smartphone camera.…”

Section: A Traditional Machine Learningmentioning

confidence: 99%

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Kavitha,

Prasanna

2023

IJRITCC

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Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches.

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“…The idea emerged when browsing literature on the application of fractal and multifractal analysis in digital imaging processing to recognize textures of different materials. Multifractal analysis is most commonly used in medicine for the discovery of changes in tissues [25]. Two basic indicators are fractal dimension used in grayscale images and lacunarity used in binary images to determine gaps in the fractal image.…”

Section: Introductionmentioning

confidence: 99%

Application of Statistical Indicators for Digital Image Analysis and Segmentation in Sorting of Agriculture Products

et al. 2018

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Food processing industry is moving forward to a full automation of all processes, especially in technological line segments which represent critical control points of food safety. One of these points is color sorting by using machine vision, where inappropriate products are removed. Most important product appearance attributes are color and texture. During food processing, the product is captured by optical devices, mostly color cameras and lasers. The aim of this paper is to investigate new eligibility criteria for digital image segmentation by using only image from the camera. The goal is to describe the texture of the product, based on chosen mathematical measures, and to allow for recognition and then classification according to the predefined range of values in an appropriate class. Images of frozen raspberry were used. Image analysis of color parameters in RGB color space and statistical tests to examine normality of data were carried out. Thereafter, one-way Anova and correlation analysis was performed. Statistically significant difference was found for the values of two indicators: entropy and new criteria were derived from standard deviation, as well as mean values of pixels for every channel, and marked as L. After determining the range of these criteria, a new algorithm was developed for image segmentation written in Matlab. One of the results of applying this algorithm is that more than 80% of good products were recognized.

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Multispectral skin patterns analysis using fractal methods

Cited by 5 publications

References 31 publications

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Application of Statistical Indicators for Digital Image Analysis and Segmentation in Sorting of Agriculture Products

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