Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review

Yassin, Nisreen I.; Omran, Shaimaa; Houby, Enas M. F. El; Allam, Hemat

doi:10.1016/j.cmpb.2017.12.012

Cited by 322 publications

(157 citation statements)

References 135 publications

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“…Systematic review (SR) has been done in [2]. In this paper authors illustrated how choosing the right medical images is important.…”

Section: Related Workmentioning

confidence: 99%

“…In other words, mammography is expensive but is the only method for screening breast cancer that has proven effective. In [2], the authors indicated that digital mammography (DM) produces the best medical image and is highly recommended for computer aided detection. In this paper, mammography scans were used for experiments, since they are of high quality.…”

Section: Introductionmentioning

confidence: 99%

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Using convolutional neural networks for breast cancer diagnosing

Naderan

Zaychenko

Napoli

2019

SRIT

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During the last few years, Convolutional Neural Networks (CNN) have been widely used in Computer-Aided Detection and the medical image analysis. The main idea of this paper is to modify CNN's architectures to achieve the better sensitivity and the precision for detecting breast cancer at an early stage compared to existing methods. For this purpose, several factors were considered before CNN training such as the data processing, model, dataset, etc. In the proposed model the following hyperparameters were the following: the dropout rate 0,2, epoch 38 and batch size 33. Besides the hyperparameters, two fully connected layers in the modified model were used. An average recall (sensitivity) in the recent works was 74%. The precision and recall of proposed model for breast cancer classification were 66,66% and 85,7%, respectively.

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“…Systematic review (SR) has been done in [2]. In this paper authors illustrated how choosing the right medical images is important.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using convolutional neural networks for breast cancer diagnosing

Naderan

Zaychenko

Napoli

2019

SRIT

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“…In the paper [4], the authors proposed a comprehensive method that includes FEM modelling based on heat transfer principles and requires 3D scanning and IR imaging for disease diagnostics. Naturally, various algorithms and methods of machine learning had been showing a sufficiently high efficiency in medicine [5,6,7,8,9]. Many studies are devoted to the analysis of the effectiveness of various decision support systems [10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Creativity in Intelligent Technologies and Data Science

2019

Communications in Computer and Information Science

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The method of microwave radiometry is one of the areas of medical diagnosis of breast cancer. It is based on analysis of the spatial distribution of internal and surface tissue temperatures, which are measured in the microwave (RTM) and infrared (IR) ranges. Complex mathematical and computer models describing complex physical and biological processes within biotissue increase the efficiency of this method. Physical and biological processes are related to temperature dynamics and microwave electromagnetic radiation. Verification and validation of the numerical model is a key challenge to ensure consistency with medical big data. These data are obtained by medical measurements of patients. We present an original approach to verification and validation of simulation models of physical processes in biological tissues. Our approach is based on deep analysis of medical data and we use machine learning algorithms. We have achieved impressive success for the model of dynamics of thermal processes in a breast with cancer foci. This method allows us to carry out a significant refinement of almost all parameters of the mathematical model in order to achieve the maximum possible adequacy. 1

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“…In order to support the radiologists’ detection rate and interpretation of breast imaging, innovative image-based features such as computer-assisted diagnostic (CAD) systems have been developed. CAD systems identify abnormalities on the breast image and can be used after or before the radiological interpretation of the image [13-16]. Even though these systems can be applied on digitized analog images, the development of digital MG made it much easier to apply these computer tools and will also enable artificial intelligence algorithm in the future.…”

Section: Mammographymentioning

confidence: 99%

“…Therefore, CAD systems should only be used as support for radiologists; the responsibility lies solely with the physician. CAD systems are nowadays widely accepted and used throughout the field of radiology [16, 40]. The image analysis of CAD systems is based on image acquisition, segmentation, processing, detection, and output [15, 16, 41].…”

Section: Computer Assistance For Image-based Diagnosis Of Bcmentioning

confidence: 99%

Digital Analysis in Breast Imaging

Ingrisch

Fallenberg

2019

Breast Care

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Breast imaging is a multimodal approach that plays an essential role in the diagnosis of breast cancer. Mammography, sonography, magnetic resonance, and image-guided biopsy are imaging techniques used to search for malignant changes in the breast or precursors of malignant changes in, e.g., screening programs or follow-ups after breast cancer treatment. However, these methods still have some disadvantages such as interobserver variability and the mammography sensitivity in women with radiologically dense breasts. In order to overcome these difficulties and decrease the number of false positive findings, improvements in imaging analysis with the help of artificial intelligence are constantly being developed and tested. In addition, the extraction and correlation of imaging features with special tumor characteristics and genetics of the patients in order to get more information about treatment response, prognosis, and also cancer risk are coming more and more in focus. The aim of this review is to address recent developments in digital analysis of images and demonstrate their potential value in multimodal breast imaging.

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Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review

Cited by 322 publications

References 135 publications

Using convolutional neural networks for breast cancer diagnosing

Using convolutional neural networks for breast cancer diagnosing

Creativity in Intelligent Technologies and Data Science

Digital Analysis in Breast Imaging

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