Advanced earlier melanoma detection algorithm using colour correlogram

Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks

“…In table 1 can be seen some of the images used for the analysis with the order given by the random number generator, with their respective test within the network mounted, and the evaluation of the result on the basis of the actual values of classification, the set of test images is obtained from the project MED-NODE[7] and the international collaboration of melanoma images project (ISIC) [17] The notations used in the table are: Comparing the level of effectiveness of the implemented neural network with techniques as Delaunay triangulation [18] arises where a percentage of success of 66.7% for Melanoma images, greater efficiency is presented in the accuracy of records of melanoma, about to the approach by natural computing technique [19], similar results are gotten, with a percentage of success of 80%, the detection of melanoma through geometric characteristics project [20] obtained a level of success to 89% with a rate of success higher than the proposed project, the technique of color correlogram [5] has a level of 91.5% efficiency with the use of a Bayesian classifier, as same than the segmentation technique for classification of the nearest neighbors [21] presenting even a level of highly superior efficiency compared to other work of the project and the proposed system, however these projects are analyzed only with efficient lighting condition images, leaving in doubt the level of efficiency in other conditions. In the presented context, results obtained in the study presented a suitable percentage of approximation of 77.50% through the structure of the proposed network, however, other techniques far outweigh it, being necessary to specify that performance tests are performed using different conditions and ideal illimunation states, artificial neural networks are one of the techniques of most renowned for the digital processing of images, every day new techniques and models based on different algorithms that can exceed the normal functioning of these and their efficiency in the classification of images appear.…”

“…Joseph and Panicker [4] propose a system of analysis of skin lesions for quick melanoma detection with an effective method of segmentation through techniques of image processing and mobile technologies, they develop a series of stages of image preprocessing, subsequent to the detection and extraction of the hair to make a direct analysis on the skin, with the obtained information in the processing of images designed, a rating system for the results obtained is done in a set 3 possibilities (benign, atypical and melanoma) producing results with a high degree of effectiveness. A very similar work is made by Soumya et al [5] where they propose an algorithm of early detection of melanoma through the use of a system of description and colour analysis, here the develop a set of phases for the image processing which includes different filters and segmentations for the analysis of them, finally they conduct tests with a set of 200 images with highly effective results (91.5%) on the implemented system.…”

Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System

“…As a result, the Fuzzy Correlation Map with its original algorithm reduces computational cost. To effectively reduce the computational cost, correlograms using small distance values have been used [29].…”

A Survey on Image Processing Techniques to Detect Melanoma

“…where p(x) is prior probability of predictor, p(c) is predictor probability of class and p(x/c) is the likelihood [4].…”

“…Then it spread deep into the skin and reaches at the blood vessels. Finally, it will spread to other part of the body and start affecting organs [4]. Therefore, in last few years, many computer aided diagnosis (CAD) systems of digital dermoscopic images have been developed for automatic detection of melanoma [2].…”