Convolutional Neural Network Strategy for Skin Cancer Lesions Classifications and Detections

被引:0
作者
Nour, Abdala [1 ]
Boufama, Boubakeur [1 ]
机构
[1] Univ Windsor, Sch Comp Sci, Windsor, ON, Canada
来源
ACM-BCB 2020 - 11TH ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS | 2020年
关键词
Skin cancer detection; Convolutional neural networks; Whale optimization; Machine learning; Image classification; MELANOMA;
D O I
10.1145/3388440.3415988
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Skin cancer is one of the most common forms of cancer that has widespread as a disease around the world. With early, accurate diagnosis, the chances of treating skin cancer are high. This has inspired us to design a deep learning model that uses a conventional neural network to automatically classify and detect different types of skin cancer images. Through this way the system takes actions to prevent and early detect skin cancer, leading to potentially the best approach for treatment. The goal of this research is to apply the systematic meta heuristic optimization and image detection techniques based on a convolutional neural network to efficiently and accurately detect and classify different types of skin lesions.
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页数:9
相关论文
共 21 条
[1]  
[Anonymous], ANACONDA OPENSOURCE, P206
[2]   Staging Uveal Melanoma with Whole-Body Positron-Emission Tomography/Computed Tomography and Abdominal Ultrasound: Low Incidence of Metastatic Disease, High Incidence of Second Primary Cancers [J].
Cohen, Victoria M. L. ;
Pavlidou, Efthymia ;
DaCosta, Joanna ;
Arora, Amit K. ;
Szyszko, Teressa ;
Sagoo, Mandeep S. ;
Szlosarek, Peter .
MIDDLE EAST AFRICAN JOURNAL OF OPHTHALMOLOGY, 2018, 25 (02) :91-95
[3]   Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes [J].
Erkol, B ;
Moss, RH ;
Stanley, RJ ;
Stoecker, WV ;
Hvatum, E .
SKIN RESEARCH AND TECHNOLOGY, 2005, 11 (01) :17-26
[4]  
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
[5]   Behavioral Counseling to Prevent Skin Cancer US Preventive Services Task Force Recommendation Statement [J].
Grossman, David C. ;
Curry, Susan J. ;
Owens, Douglas K. ;
Barry, Michael J. ;
Caughey, Aaron B. ;
Davidson, Karina W. ;
Doubeni, Chyke A. ;
Epling, John W., Jr. ;
Kemper, Alex R. ;
Krist, Alex H. ;
Kubik, Martha ;
Landefeld, Seth ;
Mangione, Carol M. ;
Silverstein, Michael ;
Simon, Melissa A. ;
Tseng, Chien-Wen .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2018, 319 (11) :1134-1142
[6]  
Guy GP, 2015, MMWR-MORBID MORTAL W, V64, P591
[7]  
Hastie T., 2015, Statistical learning with sparsity, V143, P143, DOI [10.1201/b18401, DOI 10.1201/B18401]
[8]  
Ioffe S, 2015, Arxiv, DOI arXiv:1502.03167
[9]   Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers [J].
Kawahara, Jeremy ;
Hamarneh, Ghassan .
MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2016, 2016, 10019 :164-171
[10]   Deep learning [J].
LeCun, Yann ;
Bengio, Yoshua ;
Hinton, Geoffrey .
NATURE, 2015, 521 (7553) :436-444