Skin cancer classification using Convolutional neural networks

被引:14
作者
Subramanian, R. Raja [1 ]
Achuth, Dintakurthi [1 ]
Kumar, P. Shiridi [1 ]
Reddy, Kovvuru Naveen Kumar [1 ]
Amara, Srikar [1 ]
Chowdary, Adusumalli Suchan [1 ]
机构
[1] Kalasalingam Acad Res & Educ, Dept Comp Sci & Engn, Virudunagar, Tamil Nadu, India
来源
2021 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2021) | 2021年
关键词
Skin cancer diagnosis; Deep learning; Convolutional neural networks [Standard CNN; Skin cancer; Artificial neural networks; ACCURACY;
D O I
10.1109/Confluence51648.2021.9377155
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
There is a necessary need for early detection of skin cancer and can prevent further spread in some cases of skin cancers, such as melanoma and focal cell carcinoma. Anyhow there are several factors that have bad impacts on the detection accuracy. In Recent times, the use of image processing and machine vision in the field of healthcare and medical applications is increasing at a greater phase. In this paper, we are using the Convolution neural networks to detect and classify the class of cancer based on historical data of clinical images using CNN.Some of our objectives through this research are,to build a CNN model to detect skin cancer with an accuracy of >80%,to keep the false negativity rate in the prediction to below 10%, to reach the precision of above 80% and do visualization on our Data. Simulation results show that the proposed method has superiority towards the other compared methods.
引用
收藏
页码:13 / 19
页数:7
相关论文
共 16 条
[1]  
[Anonymous], 2019, DATA SCI ENG CONFLUE
[2]  
CCA, 2018, UND SKIN CANC GUID P
[3]  
CCSsACoC,, CAN CANC STAT 2014 S
[4]   Dermatologist-level classification of skin cancer with deep neural networks [J].
Esteva, Andre ;
Kuprel, Brett ;
Novoa, Roberto A. ;
Ko, Justin ;
Swetter, Susan M. ;
Blau, Helen M. ;
Thrun, Sebastian .
NATURE, 2017, 542 (7639) :115-+
[5]  
INCA, 2018, CANC INC BRAZ
[6]  
Joshva Devadas T., 2020, INTELLIGENT SYSTEMS, V174
[7]   Diagnostic accuracy of dermoscopy [J].
Kittler, H ;
Pehamberger, H ;
Wolff, K ;
Binder, M .
LANCET ONCOLOGY, 2002, 3 (03) :159-165
[8]   A survey on deep learning in medical image analysis [J].
Litjens, Geert ;
Kooi, Thijs ;
Bejnordi, Babak Ehteshami ;
Setio, Arnaud Arindra Adiyoso ;
Ciompi, Francesco ;
Ghafoorian, Mohsen ;
van der Laak, Jeroen A. W. M. ;
van Ginneken, Bram ;
Sanchez, Clara I. .
MEDICAL IMAGE ANALYSIS, 2017, 42 :60-88
[9]   Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning [J].
Shin, Hoo-Chang ;
Roth, Holger R. ;
Gao, Mingchen ;
Lu, Le ;
Xu, Ziyue ;
Nogues, Isabella ;
Yao, Jianhua ;
Mollura, Daniel ;
Summers, Ronald M. .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016, 35 (05) :1285-1298
[10]   Cancer statistics, 2019 [J].
Siegel, Rebecca L. ;
Miller, Kimberly D. ;
Jemal, Ahmedin .
CA-A CANCER JOURNAL FOR CLINICIANS, 2019, 69 (01) :7-34