Classifying Breast Cancer Histopathological Images Using a Robust Artificial Neural Network Architecture

Zhang, Xianli; Zhang, Yinbin; Qian, Buyue; Liu, Xiaotong; Li, Xiaoyü; Wang, Xudong; Yin, Changchang; Lv, Xin; Song, Lingyun; Wang, Liang

doi:10.1007/978-3-030-17938-0_19

Cited by 23 publications

(17 citation statements)

References 22 publications

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“…A comparison study is performed between Histo-CADx and the related works that used the exact datasets to measure its effectiveness as shown in Tables 11 and 12 . Regarding the BreakHis dataset, it is clear from Table 12 that the accuracy of Histo-CADx is higher than Nahid & Kong (2018) , Sudharshan et al (2019) , Jiang et al (2019) , Muralikrishnan & Anitha (2019) , Alom et al (2019) , Vo, Nguyen & Lee (2019) , Zhang et al (2019) , Li et al (2020) , and Toğaçar et al (2020) for all magnification factors of BreakHis dataset except for the 200× where Histo-CADx has a slightly lower accuracy than Toğaçar et al (2020) . Regarding the ICIAR dataset, Table 13 verifies the competence of Histo-CADx compared to other recent related studies.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, the deep learning (DL) methods are the other category. This category can extract features from images in an automatic manner ( Zhang et al, 2019 ). Although such category has great capabilities for classifying and extracting features from huge datasets, DL is not always the perfect option in all datasets especially these having a small number of images ( Nguyen et al, 2018 ; Ragab & Attallah, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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“…Moreover, the pathological analysis is complex, time-consuming, and is subjective to the pathologist’s knowledge and experience [ 18 ]. Professional pathologists might deliver different decisions regarding the MB subtype [ 19 , 20 ]. Additionally, the limited availability of pathologists is a serious hurdle in the analysis of histopathological images.…”

Section: Introductionmentioning

confidence: 99%

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Attallah

2021

Diagnostics

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Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

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“…In contrast, Inception-V3Net and ShuffleNet obtained 93.36% with 400x magnifying factor. However, for overall average accuracy, ResNet18 achieved the best performance of 94.18%, followed by Inception-V3Net with 92.76% and ShuffleNet with 92.27%.Several studies, such as[59,76,79], applied deep learning models to diagnostics of breast cancer using real histopathological images from the BreakHis dataset[65]. Most of the previous studies focused on binary classification, although some considered multiclass classification as well.…”

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confidence: 99%

“…18: Comparing the performance of similar DNNs from this thesis and literature in binary classification.Furthermore, different data split or training parameters such as optimization algorithm, learning rate, batch size, decay factor and epoch will make a difference in the performance results.For instance,[76] used Stochastic Gradient Descent for optimization algorithm and a batch size of 32. However, in[79] the authors set the learning rate of the Stochastic Gradient Descent optimizer to 0.001. In[59] they used ResNet-50 and they divided the data into three parts for training, validation and testing and they used Stochastic Gradient Descent optimizer with a learning rate of 0.0005.In Table4.19 below, we compare the performance of different DNNs from literature and this thesis.…”

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confidence: 99%

Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

Aloyayri

Krzyżak

2020

Artificial Intelligence and Soft Computing

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Early diagnosis of breast cancer is the most reliable and practical approach to managing cancer. Computer-aided detection or computeraided diagnosis is one of the software technology designed to assist doctors in detecting or diagnose cancer and reduce mortality via using the medical image analysis with less time. Recently, medical image analysis used Convolution Neural Networks to evaluate a vast number of data to detect cancer cells or image classification. In this thesis, we implemented transfer learning from pre-trained deep neural networks ResNet18, Inception-V3Net, and ShuffleNet in terms of binary classification and multiclass classification for breast cancer from histopathological images.We use transfer learning with the fine-tuned network results in much faster and less complicated training than a training network with randomly initialized weights from scratch. Our approach is applied to image-based breast cancer classification using histopathological images from public dataset BreakHis. The highest average accuracy achieved for binary classification of benign or malignant cases was 97.11% for ResNet 18, followed by 96.78% for ShuffleNet and 95.65% for Inception-V3Net. In terms of the multiclass classification of eight cancer classes, the average accuracies for pre-trained networks are as follows. ResNet18 achieved 94.17%, Inception-V3Net 92.76% and ShuffleNet 92.27%. iv AcknowledgementFirst and foremost, all praises to Allah for blessing, protecting, and guiding me throughout my studies. I could never have accomplished this without my faith.Second, I would like to express my sincere gratitude to my supervisor Professor Adam Krzyzak for his continuous support, immense knowledge, continuous motivation, and patience.His unique personality as a supervisor and friend is the main reason behind the success of this research. The objectives of the research would not have achieved without the professional and experienced guidance and support of my supervisor.I also extend my thanks to members of the examining committee, including Dr. Thomas Fevens and Dr. Tristan Glatard, for critically evaluating my thesis and providing me with valuable comments about my research. I would like to extend my thanks Halina Monkiewicz, the program advisor of Computer Science and Software Engineering, for her support, patience and motivation.Third, I would like to express my sincere gratitude to the financial support from the Government of Saudi Arabia under the scholarship of Saudi Electronic University, which enabled me to undertake my studies, and I was lucky to obtain this opportunity. I am forever thankful to the Saudi Cultural Bureau and the Embassy of Saudi Arabia in Canada for their continued support and motivation during my journey.A heartfelt deepest gratitude expresses to my first teachers (my dear mother and my father), my brothers, and my sisters for their unconditional love, prayers, and support. Without them, this journey would not have been possible, and to them, I dedicate this milestone.Last but never least, to my l...

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Classifying Breast Cancer Histopathological Images Using a Robust Artificial Neural Network Architecture

Cited by 23 publications

References 22 publications

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

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