DU-Net: Convolutional Network for the Detection of Arterial Calcifications in Mammograms

Ghamdi, Manal Al; Abdel-Mottaleb, Mohamed; Collado-Mesa, Fernando

doi:10.1109/tmi.2020.2989737

Cited by 50 publications

(43 citation statements)

References 35 publications

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“…The calcium amount in a female's diet is not the cause of calcification. Earlier breast surgery or radiation therapy may also be the reasons of calcifications in women [10][11][12][13].…”

Section: Calcificationmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life-threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people’s inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k -means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance.

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“…The calcium amount in a female's diet is not the cause of calcification. Earlier breast surgery or radiation therapy may also be the reasons of calcifications in women [10][11][12][13].…”

Section: Calcificationmentioning

confidence: 99%

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Chaudhury

Rakhra

Memon

et al. 2021

Computational and Mathematical Methods in Medicine

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“…In Table 5, the methods mentioned above are listed according to techniques, tasks, datasets and performance metrics. AlGhamdi et al [121] showed remarkably high accuracy, specificity and sensitivity rates, respectively, 91.47%, 92.01% and 91.22%, on DDSM in calcification detection with Dense-Unet FCN. Conversely, Unet in calcification detection achieved only 70.3% sensitivity on DDSM.…”

Section: Pros and Cons Of Deep Learning Approachesmentioning

confidence: 97%

“…Performance parameters, such as sensitivity, specificity, Fi-score, etc., allowed the authors to evaluate and compare their method to state-of-the-art methods. AlGhamdi et al [121] developed a model to detect breast arterial calcifications using U-Net with dense connectivity. This model allows the reuse of computation that is already done and improves the gradient flow, leading to better model accuracy.…”

Section: Fcn For Mammogram Segmentationmentioning

confidence: 99%

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

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“…Ding et al [ 98 ] used dense block instead of codec convolutional layer to extract low-level visual features of multimodal brain tumors, and fused high-level semantic features to generate high-resolution features with fewer network parameters and fast segmentation, achieving good segmentation results. AIGhamdi et al [ 99 ] introduced dense block in U-net for breast artery calcification detection; DU-net has dense connectivity, which helps to improve computational reusability and gradient mobility and improves accuracy and training difficulty. Second, codec introduces dense block.…”

Section: Application Of Densenet In Medical Image Analysismentioning

confidence: 99%

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao

XinYu

et al. 2022

BioMed Research International

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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

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DU-Net: Convolutional Network for the Detection of Arterial Calcifications in Mammograms

Cited by 50 publications

References 35 publications

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Dense Convolutional Network and Its Application in Medical Image Analysis

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