Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms

Isasi, Alberto; Zapirain, Begoña García; Zorrilla, Amaia Méndez

doi:10.1016/j.compbiomed.2011.06.010

Cited by 66 publications

(13 citation statements)

References 11 publications

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“…This method used the 3x3 approach, aiming to improve the contrast of the image and improve the contour of the lesion, as also expressed in the study of Gola Isasi et al, (2011). After this process, it happened the filtered image binarization and the subsequent extraction of contour characteristics of the lesion (GOLA ISASI, GARCÍA ZAPIRAIN; MÉNDEZ ZORRILLA, 2011).…”

Section: Initial Processing Of Imagesmentioning

confidence: 99%

“…Another method for assessing the irregularity of borders was proposed in the study of Gola Isasi et al, (2011). Equation 6 illustrates this method developed with the selected images.…”

Section: Irregularity Of Edges (B)mentioning

confidence: 99%

“…In these cases, the predictors form entries and a set of variables, which will generate the membership of a group that will represent the outputs. Thus, neural networks are alternative instruments for the discriminate analysis of variables (RUBEGNI et al, 2002;IYATOMI et al, 2008;GOLA ISASI, GARCÍA ZAPIRAIN;MÉNDEZ ZORRILLA, 2011).…”

Section: Automatic Sorting Using Artificial Neural Networkmentioning

confidence: 99%

“…Gola Isasi et al (2011) performed the computational technique integrated to the ABCD rule with the analysis of 160 images. The present study developed an interface for the demonstration of the evaluated parameters, in which the risk predictive values for melanomas were presented.…”

Section: Automatic Sorting Using Artificial Neural Networkmentioning

confidence: 99%

“…In order to assist in the early detection of skin cancer, identifying injuries with potential malignancy, some studies have developed processing of digital image techniques to perform the automatic or semi-automatic diagnosis of these lesions (MANOUSAKI et al, 2006;CHRISTENSEN et al, 2010;PAROLIN, HERZER;JUNG, 2010;GOLA ISASI, GARCÍA ZAPIRAIN;MÉNDEZ ZORRILLA, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Software for automatic diagnostic prediction of skin clinical images based on ABCD rule

Oselame¹,

Sanches²,

Kuntze³

et al. 2017

Biosci. J.

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ABSTRACT:Cancer is responsible for about 7 million annual deaths worldwide. Among them, the melanoma type, responsible for 4% of the skin cancers, whose incidence has doubled in the last ten years. The processing of digital images has shown good potential for assistance in the early detection of melanomas. In this sense, the objective of the current study was to develop a software for clinical images processing and reach a score of accuracy higher than 95%. The ABCD rule was used as a guide for the development of computational analysis methods. MATLAB was used as programming environment for the development of the processing of digital images software. The images used were acquired from two banks of free images. They included images of melanomas (n=15) and nevi images (not cancer) (n=15). Images in RGB color channel were used, which were converted to grayscale, 8x8 median filter applications and 3x3 neighborhood approach technique. After, we proceeded to the binarization and inversion of black and white for later extraction of contour characteristics of the lesion. The classifier used was an artificial neural network of radial basis, getting accuracy for diagnosis of melanomas images of 100% and of 90.9% for not cancer images. Thus, global correction for diagnostic prediction was 95.5%. An area under the ROC graph 0.967 was achieved, suggesting a great diagnostic predictive ability. Besides, the software presents low cost use, since it can be run on most operating systems used nowadays.

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Section: Initial Processing Of Imagesmentioning

confidence: 99%

“…Another method for assessing the irregularity of borders was proposed in the study of Gola Isasi et al, (2011). Equation 6 illustrates this method developed with the selected images.…”

Section: Irregularity Of Edges (B)mentioning

confidence: 99%

Section: Automatic Sorting Using Artificial Neural Networkmentioning

confidence: 99%

Section: Automatic Sorting Using Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Software for automatic diagnostic prediction of skin clinical images based on ABCD rule

Oselame¹,

Sanches²,

Kuntze³

et al. 2017

Biosci. J.

View full text Add to dashboard Cite

show abstract

Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

Ruffano

Takwoingi

Dinnes

et al. 2018

Cochrane Database of Systematic Reviews

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An accurate and noninvasive skin cancer screening based on imaging technique

Rajput

Agrawal

Raut

et al. 2021

Int J Imaging Syst Tech

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In the last decade, the public health problem is the primary consciousness worldwide, and cancer is especially the central issue. Further, skin cancer comes in the top-3 of the world's most common cancer. We have proposed an efficient convolutional neural network (CNN) model that identifies skin cancer problems accurately. Although dataset HAM10K is used for the classification problem, its samples for each class are highly imbalanced and therefore are accountable for lower training accuracy. The AlexNet model is customized for the HAM10K data classification to address this problem. In addition, this work has presented an activation function that overcomes the vanishing gradient problem, and it is verified using the used dataset at multiple benchmark architectures. The results show better accuracy compared to the state-of-the-art activation function. Our customized CNN architecture with the proposed activation function has 98.20% accuracy for HAM10K, which is better than any other state-of-the-art models currently present. Further, precision, recall, and F-score results are also improved, which are also 98.20%.

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Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms

Cited by 66 publications

References 11 publications

Software for automatic diagnostic prediction of skin clinical images based on ABCD rule

Software for automatic diagnostic prediction of skin clinical images based on ABCD rule

Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

An accurate and noninvasive skin cancer screening based on imaging technique

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