Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Nahid, Abdullah-Al; Kong, Yinan

doi:10.3390/info9010019

Cited by 98 publications

(31 citation statements)

References 46 publications

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“…Lastly, an accuracy of 91% is achieved. In addition, in the work of [68], five DCNN models are built up, considering handcraft features and deep learning features jointly. In the experiment, the second model obtains the best performance of 92.19% accuracy.…”

Section: Related Work Of Breakhis In 2018mentioning

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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Section: Related Work Of Breakhis In 2018mentioning

confidence: 99%

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

et al. 2020

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“…They were able to improve their results by using the AlexNet network for transfer learning. The study [10] evaluated CNN with multiple handcrafted features and compared the results with those provided with raw images. They achieved their best results by using residual blocks inspired by ResNet.…”

Section: Binary Classificationmentioning

confidence: 99%

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Yari

Nguyen

2020

IEEE Access

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Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which has more and deeper layers allowing higher abstraction levels and more accurate data-based. Although deep convolutional neural networks, as a deep learning algorithm, has recently achieved promising results in data analysis, the requirement for a large amount of data prevents its use in medical data analysis since it is challenging to obtain data from the medical field. Breast cancer is a common cancer in women. To diagnose this kind of cancer, breast cell shapes in histopathology images should be examined by senior pathologists. The number of pathologists per population in the world is not enough, especially in Africa, and human mistake may occur in diagnosis procedure. After the evaluation of deep learning methods and algorithms in breast histological data processing, we tried to improve the current systems' accuracy. As a result, this study proposes two effective deep transfer learning-based models, which rely on pre-trained DCNN using a large collection of ImageNet dataset images that improve current state-of-the-art systems in both binary and multiclass classification. We transfer pre-trained weights of the ResNet50 and DesneNet121 on the Imagenet as initial weights and fine-tune these models with a deep classifier with data augmentation to detect various malignant and benign samples tissues in the two categories of binary classification and multiclass classification. The proposed models have been examined with optimized hyperparameters in magnification-dependent and magnification-independent classification modes. In the multiclass classification, the proposed system achieved up to 98% accuracy. As for binary classification, the proposed system provides up to 100% accuracy. The results outperform previous studies accuracies in all defined performance metrics in breast cancer CAD systems from histological images.

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“…Authors in [26] proposed a CNN model for breast cancer classification considering the local and frequency domain information using Histopathological images. The objective is to utilize the important information of images that are carried by the local and frequency domain information which sometime shows better accuracy for the model.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based models for diagnosis of breast cancer

Masud

Rashed

Hossain

2020

Neural Comput & Applic

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Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer. Convolutional neural networks are showing promising results to classify cancers using image datasets. There is still a lack of standard models which can claim the best model because of unavailability of large datasets that can be used for models' training and validation. Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. With this motivation, this paper considers eight different fine-tuned pre-trained models to observe how these models classify breast cancers applying on ultrasound images. We also propose a shallow custom convolutional neural network that outperforms the pre-trained models with respect to different performance metrics. The proposed model shows 100% accuracy and achieves 1.0 AUC score, whereas the best pre-trained model shows 92% accuracy and 0.972 AUC score. In order to avoid biasness, the model is trained using the fivefold cross validation technique. Moreover, the model is faster in training than the pre-trained models and requires a small number of trainable parameters. The Grad-CAM heat map visualization technique also shows how perfectly the proposed model extracts important features to classify breast cancers.

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Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Cited by 98 publications

References 46 publications

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

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

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Convolutional neural network-based models for diagnosis of breast cancer

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