Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2233
Title: A Convolutional Neural Network Based Prediction Model For Classification of Skin Cancer Images
Authors: Saini, Vanshika
Rai, Neelanjana
Sharma, Nonita
Shrivastava, Virendra Kumar
Keywords: Accuracy table
Confusion matrix
Data analysis
Datasets
Diagnostic Accuracy
Framework
Melanoma
ML algorithms
Validation approach
Issue Date: 2023
Publisher: Intelligent Systems and Machine Learning: First EAI International Conference, ICISML 2022
Citation: Vol. 470 LNICST; pp. 92-102
Abstract: There has been an unprecedented rise in the cases of skin diseases since past few decades owing to several factors. Among several skin diseases, skin cancer has also taken a steep rise and resultantly it becomes imperative to devise an efficient model to detect skin cancer. The requirement for automatic detection of skin cancer further grows owing to rise in rate of melanoma skin cancer, its expensive treatment, and its high fatality rate. Treatment of cancer cells frequently necessitates patience and manual inspection. Here, in this work authors propose an image processing and machine learning approach for skin cancer detection. It also uses a feature extraction technique to retrieve the features of the injured skin cells. The proposed model uses convolutional neural network classifier to stratify the extracted data. During the experimental evaluation, it is observed that the proposed system yields an accuracy of 77.03% and a training accuracy of 80% for the datasets available in public domain. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
URI: https://doi.org/10.1007/978-3-031-35078-8_9
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2233
ISBN: 9783031350771
9783031350788
ISSN: 1867-8211
1867-822X
Appears in Collections:Conference Papers

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