Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network
| Autor: | Taysir Hassan A. Soliman, Majid Nawaz, Adel A. Sewissy |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Computer science business.industry Deep learning Lobular carcinoma Context (language use) Image processing 02 engineering and technology Machine learning computer.software_genre medicine.disease Convolutional neural network Fibroadenoma 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Breast cancer 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence skin and connective tissue diseases Breast cancer classification business computer |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 9 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2018.090645 |
| Popis: | Breast cancer continues to be among the leading causes of death for women and much effort has been expended in the form of screening programs for prevention. Given the exponential growth in the number of mammograms collected by these programs, computer-assisted diagnosis has become a necessity. Computer-assisted detection techniques developed to date to improve diagnosis without multiple systematic readings have not resulted in a significant improvement in performance measures. In this context, the use of automatic image processing techniques resulting from deep learning represents a promising avenue for assisting in the diagnosis of breast cancer. In this paper, we present a deep learning approach based on a Convolutional Neural Network (CNN) model for multi-class breast cancer classification. The proposed approach aims to classify the breast tumors in non-just benign or malignant but we predict the subclass of the tumors like Fibroadenoma, Lobular carcinoma, etc. Experimental results on histopathological images using the BreakHis dataset show that the DenseNet CNN model achieved high processing performances with 95.4% of accuracy in the multi-class breast cancer classification task when compared with state-of-the-art models. |
| Databáze: | OpenAIRE |
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