Evolving strategies for the development and evaluation of a computerised melanoma image analysis system

Katapadi, Aashish; Celebi, M. Emre; Trotter, Shannon C.; Gürcan, Metin N.

doi:10.1080/21681163.2016.1277785

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Many works are being created that allow for skin lesions diagnosis from dermatoscopic images. Researchers in the work on feature extration based on CNN architecture focus on geometry-based [20], color-based data augmentation [25] and ensemble of network architectures [17].…”

Section: Features Extractionmentioning

confidence: 99%

Neural Networks From Keras in Skin Lesion Diagnostic

Michalska

2022

IAPGOS

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Abstract. Melanoma is currently one of the most dangerous skin diseases, in addition many others appear in the population. Scientists are developing techniques for early non-invasive skin lesions diagnosis from dermatoscopic images, for this purpose neural networks are increasingly used. Many tools are being developed to allow for faster implementation of the network, including the Keras package. . The article presents selected methods of diagnosing skin diseases, including the process of classification, features selection, extracting the skin lesion from the whole image.The described methods have been implemented using deep neural networks available in the Keras package. The article draws attention to the effectiveness, specificity, accuracy of classification based on available data sets, attention was paid to tools that allow for more effective operation of algorithms.

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Section: Features Extractionmentioning

confidence: 99%

Neural Networks From Keras in Skin Lesion Diagnostic

Michalska

2022

IAPGOS

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“…The resulting methods should be included as a component in the system that can be implemented. The analysis of such systems is presented in [15]. Using a smartphone to analyze the image shown in [16].…”

Section: A Related Workmentioning

confidence: 99%

Analysis of Skin Marks Through the Use of Intelligent Things

Połap

2019

IEEE Access

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Melanoma is one of the more frequently encountered cancers, which quite often depends on exposure to ultraviolet radiation occurring in solar radiation. Of course, an additional factor is individual immunity and genetics. It is important to discover the possible spread of the cancer, which in this case depends on the observation of the skin, and above all the marks. The quickest possible detection of neoplastic changes can result in a chance to prolong life. In the era of mobility, where everyone has a camera built into the phone and access to the Internet, it is possible to analyze signs by an application that would analyze the skin. In the case of detected exceptions, the application would exchange information with a publicly available database and consult with a dermatologist about the possible need for urgent hispatological examination. In this paper, we propose a model of such a system based on intelligent things along with a detailed description of individual components. As a classification component, a convolutional classifier was proposed, which accepts not only image data, but also numerical one. The analysis of the proposed solution has been tested and discussed due to numerous advantages and disadvantages of such a solution nowadays.INDEX TERMS Feature extraction, image processing, deep learning, Internet of Things.

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Automatic Melanoma Detection System (AMDS): A State-of-the-Art Review

Kumar

2020

Communications in Computer and Information Science

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Evolving strategies for the development and evaluation of a computerised melanoma image analysis system

Cited by 5 publications

References 31 publications

Neural Networks From Keras in Skin Lesion Diagnostic

Neural Networks From Keras in Skin Lesion Diagnostic

Analysis of Skin Marks Through the Use of Intelligent Things

Automatic Melanoma Detection System (AMDS): A State-of-the-Art Review

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