Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges

Murtaza, Ghulam; Shuib, Liyana; Wahab, Ainuddin Wahid Abdul; Mujtaba, Ghulam; Nweke, Henry Friday; Al-Garadi, Mohammed Ali; Zulfiqar, Fariha; Raza, Ghulam; Azmi, Nor Aniza

doi:10.1007/s10462-019-09716-5

Cited by 223 publications

(106 citation statements)

References 139 publications

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“…The motivation is to clarify the development history of ANNs, understand the popular technology and trend of ANN applications, and discover the future potential of ANNs in the BHIA field. As far as we know, there exist some survey papers that summarize papers related to the BHIA work (e.g., the reviews in [7], [9], [12], [17]- [33]). In the following part, we go through the survey papers that are related to the BHIA work.…”

Section: B Motivation Of Our Review Papermentioning

confidence: 99%

“…The survey of [7] publishes a review about the classification tasks of breast cancer on deep learning. The author reviews five aspects, namely datasets used, various medical imaging modalities exploited, image pre-processing techniques, types of DNNs, and the performance metrics used to construct and evaluate breast cancer classification models.…”

Section: B Motivation Of Our Review Papermentioning

confidence: 99%

“…However, invasive carcinoma is a malignant tumor that tends to spread in other organs. There are many techniques for breast cancer detection, such as X-ray mammography [4], 3-D Ultrasound (US) [5], Computed Tomography (CT), Positron Emission Tomography (PET) [6], Magnetic Resonance Imaging (MRI) [7], and breast temperature measurement [8]. However, pathological diagnosis is often regarded as a ''golden standard'' [9].…”

Section: Introductionmentioning

confidence: 99%

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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: B Motivation Of Our Review Papermentioning

confidence: 99%

Section: B Motivation Of Our Review Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Using the deep learning method in segmentation, it has proved the efficiency in time processing. This idea is deeply presented in the recent research of Ghulam Murtaza et al [37]. The research focuses on breast cancer classification using medical imaging multimodalities through the state-of-the-art artificial deep neural network approaches.…”

Section: Current Researchesmentioning

confidence: 99%

Application of Geometric Modeling in Visualizing the Medical Image Dataset

Nguyen

Tran

2020

SN COMPUT. SCI.

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Geometric modeling is a research based on computational geometry for processing the 3D objects in computer graphics or image processing. Rendering the graphical objects in medical applications is an interesting research that can help doctors have an exact decision in their diagnostic method. Application of geometric modeling in visualization of medical data is considered as a tool to support treatment. In the field of big data analysis and data science, the methods, tools and techniques are essential and important to analyze a massive amount of information and make data-driven decisions. These data are first analyzed and computed depending on the criteria and purpose of users. They are then visualized or simulated using visual elements like charts, graphs or maps to explore, understand their characteristics and data structure. Which method or solution is best for rendering and visualizing the analyzed data? That is still a question and even a challenge to the researchers. In this article, we apply geometric modeling to build a web-based application for visualizing a medical image dataset. This is an improved version of the selected paper in the previous work[1]. We first review several researches in the fields of computer graphics, images processing, geometric modeling and their applications; then we combine all of them and propose a method to develop our application for reconstructing the medical data objects. The input data are a Dicom (Digital Imaging and Communications in Medicine) dataset captured from CT (computed tomography) scanner or MRI (Magnetic Resonance Imaging). The output are 2D and 3D models that can be displayed and handled in different ways on the web interface. In comparison with other existing applications, our method shown the advantages of its using in practice.

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“…Dermoscopic image is another useful modality and is dermoscopic images that use to skin lesion [42], [43]. Breast cancer (BrC) image is another type of well-known cancers that rely on such medical image modalities as mammography which known as X-ray of breast, US which is called sonogram [44]. Furthermore, histology images use to determine multi-size and discriminative patches to classify BrC [45].…”

Section: Medical Image Modalitiesmentioning

confidence: 99%

Review Research of Medical Image Analysis Using Deep Learning

Mohammed

Al-Ani

2020

UHD J SCI TECH

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In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

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Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges

Cited by 223 publications

References 139 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

Application of Geometric Modeling in Visualizing the Medical Image Dataset

Review Research of Medical Image Analysis Using Deep Learning

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