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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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