NDLI: Supervised classification of dermoscopic images using optimized fuzzy clustering based Multi-Layer Feed-forward Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Mehta, A. Parihar, A.S. Mehta, N.
Copyright Year	2015
Description	Author affiliation: Dept. Of Comput. Sci. & Eng., IES-IPS Acad., Indore, India (Mehta, N.) \|\| Dept. Of Comput. Sci. & Eng., Shushila Devi Bansal Coll. of Technol., Indore, India (Mehta, A.; Parihar, A.S.)
Abstract	Medical image segmentation is the utmost imperative procedure to assist in the conception of the structure of prominence in medical images. Malignant melanoma is the most recurrent type of skin cancer but it is remediable, if diagnosed at a premature stage. Dermoscopy is a non-invasive, diagnostic tool having inordinate possibility in the prompt diagnosis of malignant melanoma, but their interpretation is time overwhelming. Numerous algorithms were established for classification and segmentation of Dermoscopic images. This Research work proposes the tasks of extracting, classifying and segmenting the Dermoscopic image using a more Efficient supervised learning approach, I.e., Multi-Layer Feed-forward Neural Network for more accurate and computationally efficient segmentation. The features are extracted from the Dermoscopic image using Genetically Optimized Fuzzy C-means clustering approach and these accurate features are used to train the multi-layer classifier. The trained network are used for segmentation of malignant melanoma from the skin. The results will be compare with the ground truth images and their performance is evaluate after completion of work. The results will be in form of various validation parameters and should outperform the existing supervised learning approaches.
Starting Page	1
Ending Page	6
File Size	436472
Page Count	6
File Format	PDF
e-ISBN	9781479981649
DOI	10.1109/IC4.2015.7375719
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy Clustering Multi-Layer Network Malignant tumors Artificial Intelligence Genetic Algorithm Malignant Melanoma Dermoscopic Image Segmentation Skin cancer classification technique Image segmentation Skin Cancer Feed-Forward Neural Network Neural networks Feature extraction Skin
Content Type	Text
Resource Type	Article

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