Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2

Ferreira, Carlos; Melo, Tânia; Sousa, Pedro; Meyer, Maria Inês; Shakibapour, Elham; Costa, Pedro; Campilho, Aurélio

doi:10.1007/978-3-319-93000-8_86

Cited by 85 publications

(32 citation statements)

References 8 publications

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“…Nawaz, W., et al [28] 75.73 -Golatkar, A., et al [64] 79 85 Mahbod A., et al [29] -88.5 Roy, K., et al [65] 77.4 90 Wang, Z., et al [42] 87 92 Ferreira, C. A., et al [66] -93 Our model with Experiment 4 (same domain transfer Learning) 90.5 97.4…”

Section: Methods Patch-wise (%) Image-wise (%)mentioning

confidence: 94%

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

et al. 2020

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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.

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Section: Methods Patch-wise (%) Image-wise (%)mentioning

confidence: 94%

“…Image-Wise (%) Nawaz W., et al [28] 81.25 Awan R, et al [67] 83.33 Guo Y., et al [68] 87.5 Vang Y.S., et al [69] 87.5 Sarker M., I et al [70] 89 Ferreira C.A., et al [66] 90 Kassani S. H., et al [41] 92.5 Wang, Z., et al [42] 93 Our model with Experiment 4 (same domain-transfer Learning) 96.1…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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“…For this classification task, the Inception-ResNet-v2 was used by Ferreria et al [ 20 ]. The classification layers of the base model were replaced by a global average pooling layer, a dense (or fully connected) layer with 256 neurons, a dropout layer with a dropout rate of 0.5, and a final dense layer of 4 neurons.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, the input size of the network was changed to 244 × 244. Reshaping the images does not significantly impact the form of the cellular structures; however, it does reduce computational cost [ 20 ]. The authors did not incorporate stain normalization into their experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Hematoxylin and Eosin‐Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets

Munien

Viriri

2021

Computational Intelligence and Neuroscience

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Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.

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“…In [101], an Inception ResNet-V2 is proposed to classify histological images of breast cancer through transfer learning, fine-tuning, and data augmentation. Out of 100 images in each class, 70, 20, and 10 images are randomly selected for training, testing, and validation.…”

Section: ) ''Bach'' Tasksmentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2

Cited by 85 publications

References 8 publications

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

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

Classification of Hematoxylin and Eosin‐Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

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