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AI-Enabled Support System for Melanoma Detection and Classification
by
Kumar, Avneesh
, Johri, Prashant
, Sen Saxena, Vivek
in
Algorithms
/ Artificial intelligence
/ Classification
/ Data mining
/ Decision making
/ Feature extraction
/ Image processing
/ Image segmentation
/ Lesions
/ Machine learning
/ Melanoma
/ Neural networks
/ Performance measurement
/ Skin
/ Skin cancer
/ Support systems
2021
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AI-Enabled Support System for Melanoma Detection and Classification
by
Kumar, Avneesh
, Johri, Prashant
, Sen Saxena, Vivek
in
Algorithms
/ Artificial intelligence
/ Classification
/ Data mining
/ Decision making
/ Feature extraction
/ Image processing
/ Image segmentation
/ Lesions
/ Machine learning
/ Melanoma
/ Neural networks
/ Performance measurement
/ Skin
/ Skin cancer
/ Support systems
2021
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Do you wish to request the book?
AI-Enabled Support System for Melanoma Detection and Classification
by
Kumar, Avneesh
, Johri, Prashant
, Sen Saxena, Vivek
in
Algorithms
/ Artificial intelligence
/ Classification
/ Data mining
/ Decision making
/ Feature extraction
/ Image processing
/ Image segmentation
/ Lesions
/ Machine learning
/ Melanoma
/ Neural networks
/ Performance measurement
/ Skin
/ Skin cancer
/ Support systems
2021
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AI-Enabled Support System for Melanoma Detection and Classification
Journal Article
AI-Enabled Support System for Melanoma Detection and Classification
2021
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Overview
Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.
Publisher
IGI Global
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