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Melanoma Detection Based on Hybridization of Extended Feature Space

Anuj Kumar, Shakti Kumar*

DCSA, Panjab University, Chandigarh, 160014, India

* Corresponding Author: Shakti Kumar. Email: email

Intelligent Automation & Soft Computing 2023, 37(2), 2175-2198.


Melanoma is a perfidious form of skin cancer. The study offers a hybrid framework for the automatic classification of melanoma. An Automatic Melanoma Detection System (AMDS) is used for identifying melanoma from the infected area of the skin image using image processing techniques. A larger number of pre-existing automatic melanoma detection systems are either commercial or their accuracy can be further improved. The research problem is to identify the best preprocessing technique, feature extractor, and classifier for melanoma detection using publically available MED-NODE data set. AMDS goes through four stages. The preprocessing stage is for noise removal; the segmentation stage is for extracting lesions from infected skin images; the feature extraction stage is for determining the features like asymmetry, border, and color, and the classification stage is to classify the lesion as benign or melanoma. The infected input image for the AMDS may contain impurities such as noise, illumination, artifacts, and hairs. In the proposed methodology an algorithm LePrePro is proposed for the preprocessing stage for denoising and brightness cum contrast normalization and another algorithm LeFET is proposed for extending the feature vector space in the feature extraction stage using a hybrid approach. In the study, a novel approach has been proposed in which different classifiers, feature extractions, and data preprocessing steps of the AMDS are compared. In a conclusion, this comparison revealed that on experimentation using Med-Node and ISIC 2017 Dataset, the best results included Gaussian blur as the best data preprocessing step, Extended feature vector which is the combination of Hue Saturation Value (HSV), and Local Binary Pattern (LBP) was the best feature extraction method, and the ensemble bagged tree was the best classification technique on the Med-Node data sets with 99% Area Under the Receiver Operating Characteristic Curve (AUC), 93.52% accuracy, 90.82% sensitivity, and 98.36% specificity in the proposed automatic melanoma detection system.


Cite This Article

A. Kumar and S. Kumar, "Melanoma detection based on hybridization of extended feature space," Intelligent Automation & Soft Computing, vol. 37, no.2, pp. 2175–2198, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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