Benign and malignant breast tumors classification based on region growing and CNN segmentation

By Rahimeh Rouhi, Mehdi M. Jafari, Shohreh Kasaei, Peiman Keshavarzian. Expert systems with applications, 2015

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Neurology

View Paper: https://www.semanticscholar.org/paper/c6db34ade32b3681a92068b22a354903b2953d52

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohammed Ali Al-Garadi
2. Mohammad Sohel Rahman
3. Quanzheng Li
4. Xin Zhu
5. Henry Friday Nweke
6. Weihao Weng
7. Nabil Ibtehaz
8. A. Ben Hamza

In Neurology

1. Mohammad Sohel Rahman
2. Xin Zhu
3. Weihao Weng
4. Nabil Ibtehaz
5. A. Ben Hamza
6. Hasib Zunair

Citations in the corpus

• INet: Convolutional Networks for Biomedical Image Segmentation (2021, background): “In practice, diagnosis only leverages the features extracted from segmented images [19].”
• Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “Similarly, Rouhi et al. (2015) cropped the ROI of abnormal tissues and mass regions before BrC classification.”
• Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, methodology): “Further, for the task of automatic tumor segmentation, CNNs have been applied to segmentation of tumors in brain [5], [12], [13], liver [14], breast [15], lung [16], [17],”
• MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation (2019, methodology): “Furthermore, often the segmented images are used to compute various features that may be leveraged in the diagnosis [6].”