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Breast Cancer Classification in Histopathological Images using Convolutional Neural Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rahhal, Mohamad Mahmoud Al |
| Copyright Year | 2018 |
| Abstract | Computer based analysis is one of the suggested means that can assist oncologists in the detection and diagnosis of breast cancer. On the other hand, deep learning has been promoted as one of the hottest research directions very recently in the general imaging literature, thanks to its high capability in detection and recognition tasks. Yet, it has not been adequately suited to the problem of breast cancer so far. In this context, I propose in this paper an approach for breast cancer detection and classification in histopathological images. This approach relies on a deep convolutional neural networks (CNN), which is pretrained on an auxiliary domain with very large labelled images, and coupled with an additional network composed of fully connected layers. The network is trained separately with respect to various image magnifications (40x, 100x, 200x and 400x). The results presented in the patient level achieved promising scores compared to the state of the art methods. |
| File Format | PDF HTM / HTML |
| DOI | 10.14569/IJACSA.2018.090310 |
| Volume Number | 9 |
| Alternate Webpage(s) | http://thesai.org/Downloads/Volume9No3/Paper_10-Breast_Cancer_Classification_in_Histopathological_Images.pdf |
| Alternate Webpage(s) | https://doi.org/10.14569/IJACSA.2018.090310 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
