ERNet : Enhanced ResNet for classification of breast histopathological images

• Published in: Electronic letters on computer vision and image analysis Vol. 22; no. 2; pp. 53 - 68
• Main Authors: J, Kamalakannan, R K, Chandana Mani
• Format: Journal Article
• Language: English
• Published: Computer Vision Center Press 14-03-2024
• ISSN: 1577-5097; 1577-5097
• DOI: 10.5565/rev/elcvia.1614

Inspite of expeditious approaches in field of breast cancer, histopathological analysis is considered as gold standard in diagnosis of cancer. Researchers are working tremendously to automate the detection and analysis of breast histology images, which confess in improving the accuracy and also induce the mimisation of processing time. Deep learning models are providing greater contribution in solving several image classification tasks. In this paper we propose a model to classify breast histological images, which is redesigned from existing ResNet architecture that minimises model parameters and increase computational efficiency. This approach uses enhanced ResNet connection instead of identity shortcut connection used in ResNet architecture. We apply our proposed method on BreakHis dataset and achieve an accuracy around 95.92 %.  The numerical results show that our proposed approach outperforms the previous methods with respect to sensitivity and accuracy.