Generating Knowledge-Guided Discriminative Features Using Genetic Programming for Melanoma Detection

Ain, Qurrat Ul; Al-Sahaf, Harith; Xue, Bing; Zhang, Mengjie

doi:10.26686/wgtn.12616760

Cited by 3 publications

(1 citation statement)

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“…This system classified the images with 68% specificity and 94%accuracy. Garnavi et al (2012) and Ain et al (2020) proposed a system that uses wavelet decomposition method for extracting the textural features and shape indexes to determine the geometry features. The gain ratio method is used for feature selection.…”

Section: Introductionmentioning

confidence: 99%

Novel approach for melanoma detection through iterative deep vector network

Vani

Kavitha²,

Subitha

2021

J Ambient Intell Human Comput

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Medical imaging is an important field of research used for the diagnosis and prediction of diseases. Melanoma is considered as one of the hazardous types of cancers and if detected in early stages, it can be cured easily using simple methods. By using clinical examination, it is difficult to predict melanoma at early stages with high accuracy. This paper proposes a novel strategy for the detection of melanoma by skin malignant growth and also proposes a method for early prediction. The proposed system is based on Deep learning algorithm for the prediction of the affected area and type of melanoma using the metrics precision, accuracy, recall and F1 score. The pre-processing methods are utilized for enhancing the image. The Active contour segmentation process differentiates the infected regions from the healthy skin regions. SOM and CNN classifiers are used for the process of classification of melanoma. A randomly chosen sample of 500 images are taken, 350 images are used as the training dataset and 150 images are used as a testing dataset, for which the proposed system showed high efficiency in the detection of melanoma with a greater accuracy of 90%.

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Section: Introductionmentioning

confidence: 99%