| Title: | A Deep CNN Model for Skin Cancer Detection and Classification |
| Authors: | Junayed, Masum Shah Anjum, Nipa Noman, Abu Islam, Baharul |
| Citation: | WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 71-80. |
| Issue Date: | 2021 |
| Publisher: | Václav Skala - UNION Agency |
| Document type: | conferenceObject konferenční příspěvek |
| URI: | http://hdl.handle.net/11025/45011 |
| ISBN: | 978-80-86943-34-3 |
| ISSN: | 2464-4617 2464–4625(CD/DVD) |
| Keywords: | rakovina kůže;dataset;augmentace dat;konvoluční neurální síť;lékařský obraz;počítačové vidění |
| Keywords in different language: | skin cancer;dataset;data augmentation;deep CNN;medical image;computer vision |
| Abstract in different language: | Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection ofskin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-basedmethods help dermatologists in classifying medical images. This paper proposes a deep learning-based modelto detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, wecollected a dataset that includes four skin cancer image data before applying them in augmentation techniques toincrease the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data,our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNetby 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while beingcomputationally comparable. |
| Rights: | © Václav Skala - UNION Agency |
| Appears in Collections: | WSCG 2021: Full Papers Proceedings |
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