Mammogram breast cancer CAD systems for mass detection and classification: a review

Hassan, Nada M.; Hamad, Safwat; Mahar, Khaled

doi:10.1007/s11042-022-12332-1

Cited by 58 publications

(27 citation statements)

References 92 publications

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“…Hassan, et al, 16 provided a dataset-based quantitative comparison of the most recent techniques and the most commonly-used evaluation metrics for the breast cancer CAD systems. The survey also highlighted challenges and limitations of the current breast cancer detection and classification techniques.…”

Section: Discussionmentioning

confidence: 99%

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Bhattacharjee

Poddar

Bhaumik

et al. 2022

Kesmas: Jurnal Kesehatan Masyarakat Nasional

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Various images from massive image databases extract inherent, implanted information or different examples explicitly found in the images. These images may help the community in initial self-screening breast cancer, and primary health care can introduce this method to the community. This study aimed to review the different methods of abnormal mass detection in digital mammograms. One of best methods for the detection of breast malignancy and discovery at a nascent stage is digital mammography. Some of the mammograms with excellent images have a high intensity of resolution that enables preparing images with high computations. The fact that medical images are so common on computers is one of the main things that helps radiologists make diagnoses. Image preprocessing highlights the portion after extraction and arrangement in computerized mammograms. Moreover, the future scope of examination for paving could be the way for a top invention in computer-aided diagnosis (CAD) for mammograms in the coming years. This also distinguished CAD that helped identify strategies for mass widely covered in the study work. However, the identification methods for structural deviation in mammograms are complicated in real-life scenarios. These methods will benefit the public health program if they can be introduced to primary health care's public health screening system. The decision should be made as to which type of technology fits the level of the primary health care system.

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Section: Discussionmentioning

confidence: 99%

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Bhattacharjee

Poddar

Bhaumik

et al. 2022

Kesmas: Jurnal Kesehatan Masyarakat Nasional

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“…In 2019, WHO reported that the first or second major reason for death earlier than the age of 70 is cancer, in 112 of 183 nations. It is ranked third or fourth in the other 23 countries [1]. It causes an irregular growth of cells and is frequently named depending on the part of the body in which it occurs.…”

Section: Introductionmentioning

confidence: 99%

CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis

2022

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In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world’s 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN models may become successfully fine-tuned to obtain improved findings. To conduct the automatic detection of BC by the CBIS-DDSM dataset, a CNN model, namely CoroNet, is proposed. It relies on the Xception architecture, which has been pre-trained on the ImageNet dataset and has been fully trained on whole-image BC according to mammograms. The convolutional design method is used in this paper, since it performs better than the other methods. On the prepared dataset, CoroNet was trained and tested. Experiments show that in a four-class classification, it may attain an overall accuracy of 94.92% (benign mass vs. malignant mass) and (benign calcification vs. malignant calcification). CoroNet has a classification accuracy of 88.67% for the two-class cases (calcifications and masses). The paper concluded that there are promising outcomes that could be improved because more training data are available.

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“…The widely used medical breast images are Mammography [5], Positron Emission Tomography (PET) [6], Magnetic Resonance Imaging (MRI) [7], Computed Tomography (CT) [8] and Ultrasound [9]. Mammography is one of the most widely used screening methods for breast cancer [10,11] due to its low cost, better sensitivity and specificity for breast lesions, and excellent ability to detect lesions of the breast in the early stage of breast cancer [12]. A mammography breast sample contains two images with two views, that is, Craniocaudal (CC) and Mediolateral Oblique (MLO) mammographic views.…”

Section: Introductionmentioning

confidence: 99%

Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography

et al. 2022

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Breast cancer accounts for the largest number of patients among all cancers in the world. Intervention treatment for early breast cancer can dramatically extend a woman's 5‐year survival rate. However, the lack of public available breast mammography databases in the field of Computer‐aided Diagnosis and the insufficient feature extraction ability from breast mammography limit the diagnostic performance of breast cancer. In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. A multi‐view network is designed to extract the complementary information between the Craniocaudal (CC) and Mediolateral Oblique (MLO) mammographic views of a breast mass. For the different predictions of the features extracted from the CC view and MLO view of the same breast mass, the proposed algorithm forces the network to extract the consistent features from the two views by the cross‐entropy function with an added consistent penalty term. To exploit the discriminative features from the insufficient mammographic images, the authors learnt an encoder in the classification model to learn the invariable representations from the mammographic breast mass by Supervised Contrastive Learning (SCL) to weaken the side effect of colour jitter and illumination of mammographic breast mass on image quality degradation. The experimental results of all the classification algorithms mentioned in this paper on Digital Database for Screening Mammography (DDSM) illustrate that the proposed algorithm greatly improves the classification performance and diagnostic speed of mammographic breast mass, which is of great significance for breast cancer diagnosis.

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Mammogram breast cancer CAD systems for mass detection and classification: a review

Cited by 58 publications

References 92 publications

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

Review of Different Methods of Abnormal Mass Detection in Digital Mammograms

CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis

Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography

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