Abstract:
The number of breast cancer cases and mortality rate has been on the rise globally. For effective treatment and curbing the mortality rate, early diagnosis is paramount. ...Show MoreMetadata
Abstract:
The number of breast cancer cases and mortality rate has been on the rise globally. For effective treatment and curbing the mortality rate, early diagnosis is paramount. To evaluate the effectiveness of treatment, a fundamental step is histopathological examination of the state of nuclei. However this is a challenging task for various reasons. Accordingly, we perform nuclei detection in breast cancer histopathology images using RetinaNet with a ResNet-152 as the feature extractor. The data used for training and validation of the model was carefully collected at our partner teaching hospital. A mean Average Precision (mAP) of 78% was achieved by optimizing the Focal Loss. Thus outperforming current state-of-the-art methods which have been reported in the literature.
Date of Conference: 09-11 June 2021
Date Added to IEEE Xplore: 19 July 2021
ISBN Information:
Print on Demand(PoD) ISSN: 2165-0608