Pre‐trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology. Issue 3 (28th January 2020)

• Record Type: Journal Article
• Authors: Saxena, Shweta; Shukla, Sanyam; Gyanchandani, Manasi
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 3(2020)
• Page Start: 577
• Page End: 591
• Publication Date: 2020-01-28
• Subjects: breast cancer -- computer‐aided diagnosis -- histopathology -- pre‐trained convolutional neural network; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22399
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Several researchers are trying to develop different computer‐aided diagnosis system for breast cancer employing machine learning (ML) methods. The inputs to these ML algorithms are labeled histopathological images which have complex visual patterns. So, it is difficult to identify quality features for cancer diagnosis. The pre‐trained Convolutional Neural Networks (CNNs) have recently emerged as an unsupervised feature extractor. However, a limited investigation has been done for breast cancer recognition using histopathology images with CNN as a feature extractor. This work investigates ten different pre‐trained CNNs for extracting the features from breast cancer histopathology images. The breast cancer histopathological images are obtained from publicly available BreakHis dataset. The classification models for the different feature sets, which are obtained using different pre‐trained CNNs in consideration, are developed using a linear support vector machine. The proposed method outperforms the other state of art methods for cancer detection, which can be observed from the results obtained.