Stain Normalized Breast Histopathology Image Recognition using
Convolutional Neural Networks for Cancer Detection
- URL: http://arxiv.org/abs/2201.00957v1
- Date: Tue, 4 Jan 2022 03:09:40 GMT
- Title: Stain Normalized Breast Histopathology Image Recognition using
Convolutional Neural Networks for Cancer Detection
- Authors: Sruthi Krishna, Suganthi S.S, Shivsubramani Krishnamoorthy, Arnav
Bhavsar
- Abstract summary: Recent advances have shown that the convolutional Neural Network (CNN) architectures can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection.
We consider some contemporary CNN models for binary classification of breast histopathology images.
We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images.
- Score: 9.826027427965354
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Computer assisted diagnosis in digital pathology is becoming ubiquitous as it
can provide more efficient and objective healthcare diagnostics. Recent
advances have shown that the convolutional Neural Network (CNN) architectures,
a well-established deep learning paradigm, can be used to design a Computer
Aided Diagnostic (CAD) System for breast cancer detection. However, the
challenges due to stain variability and the effect of stain normalization with
such deep learning frameworks are yet to be well explored. Moreover,
performance analysis with arguably more efficient network models, which may be
important for high throughput screening, is also not well explored.To address
this challenge, we consider some contemporary CNN models for binary
classification of breast histopathology images that involves (1) the data
preprocessing with stain normalized images using an adaptive colour
deconvolution (ACD) based color normalization algorithm to handle the stain
variabilities; and (2) applying transfer learning based training of some
arguably more efficient CNN models, namely Visual Geometry Group Network
(VGG16), MobileNet and EfficientNet. We have validated the trained CNN networks
on a publicly available BreaKHis dataset, for 200x and 400x magnified
histopathology images. The experimental analysis shows that pretrained networks
in most cases yield better quality results on data augmented breast
histopathology images with stain normalization, than the case without stain
normalization. Further, we evaluated the performance and efficiency of popular
lightweight networks using stain normalized images and found that EfficientNet
outperforms VGG16 and MobileNet in terms of test accuracy and F1 Score. We
observed that efficiency in terms of test time is better in EfficientNet than
other networks; VGG Net, MobileNet, without much drop in the classification
accuracy.
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