Volume 26, Issue 1 (April 2019)
J Birjand Univ Med Sci. 2019, 26(1): 44-53 |
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Fooladi S, Farsi H, Mohamadzadeh S. Detection and classification of skin cancer using deep learning. J Birjand Univ Med Sci. 2019; 26 (1) :44-53
URL: http://journal.bums.ac.ir/article-1-2533-en.html
URL: http://journal.bums.ac.ir/article-1-2533-en.html
1- Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
2- Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran. , s.mohamadzadeh@birjand.ac.ir
2- Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran. , s.mohamadzadeh@birjand.ac.ir
Abstract: (7207 Views)
Background and Aim: Skin cancer has grown dramatically over the past decades, and the importance of early treatment is increasing day by day. The purpose of this study is to use deep neural networks to create an auto-diagnosis system for melanoma, in which data is directly controlled as part of a deep learning process.
Materials and Methods: In this paper, studies on related pictures of skin cancer were performed. For the diagnosis of benign or malignant skin cancer, the deep neural network classifier is used with the help of the Tensorflow framework and the use of the Keras libraries. The dataset which are used in this study consist 70 images of melanoma and 100 images of benign moles. In the proposed model, 80% of the database images are used for training and 20% of the database images are selected for testing.
Results: The proposed method offers a higher detection accuracy than other existing methods, which has increased the accuracy of diagnosis in most cases by more than 10%. The high accuracy of the diagnosis and classification and the speed of convergence to the final result are the characteristics of this Research Compared to other Research.
Conclusion: An automatic system based on deep learning is presented to identify and categorize skin cancer which provides high accuracy and speed.
Materials and Methods: In this paper, studies on related pictures of skin cancer were performed. For the diagnosis of benign or malignant skin cancer, the deep neural network classifier is used with the help of the Tensorflow framework and the use of the Keras libraries. The dataset which are used in this study consist 70 images of melanoma and 100 images of benign moles. In the proposed model, 80% of the database images are used for training and 20% of the database images are selected for testing.
Results: The proposed method offers a higher detection accuracy than other existing methods, which has increased the accuracy of diagnosis in most cases by more than 10%. The high accuracy of the diagnosis and classification and the speed of convergence to the final result are the characteristics of this Research Compared to other Research.
Conclusion: An automatic system based on deep learning is presented to identify and categorize skin cancer which provides high accuracy and speed.
Type of Study: Original Article |
Subject:
Oncology
Received: 2018/07/8 | Accepted: 2018/10/27 | ePublished: 2019/03/17
Received: 2018/07/8 | Accepted: 2018/10/27 | ePublished: 2019/03/17
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