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Article

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

1
Faculty of Science & Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
2
Al-Nidhal Campus, University of Information Technology & Communications, Baghdad 00964, Iraq
3
College of Computer Science and Information Technology, University of Sumer, Thi Qar 64005, Iraq
4
School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
5
Faculty of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 445; https://doi.org/10.3390/electronics9030445
Received: 24 January 2020 / Revised: 27 February 2020 / Accepted: 4 March 2020 / Published: 6 March 2020
(This article belongs to the Special Issue Deep Learning for Medical Images: Challenges and Solutions)
Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset. View Full-Text
Keywords: breast cancer; convolutional neural network; transfer learning; classification breast cancer; convolutional neural network; transfer learning; classification
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MDPI and ACS Style

Alzubaidi, L.; Al-Shamma, O.; Fadhel, M.A.; Farhan, L.; Zhang, J.; Duan, Y. Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model. Electronics 2020, 9, 445. https://doi.org/10.3390/electronics9030445

AMA Style

Alzubaidi L, Al-Shamma O, Fadhel MA, Farhan L, Zhang J, Duan Y. Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model. Electronics. 2020; 9(3):445. https://doi.org/10.3390/electronics9030445

Chicago/Turabian Style

Alzubaidi, Laith, Omran Al-Shamma, Mohammed A. Fadhel, Laith Farhan, Jinglan Zhang, and Ye Duan. 2020. "Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model" Electronics 9, no. 3: 445. https://doi.org/10.3390/electronics9030445

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