Breast cancer detection in mammograms using convolutional neural network

Charan, S. G.; Khan, Muhammad Jaleed; Khurshid, Khurram

doi:10.1109/icomet.2018.8346384

Cited by 111 publications

(57 citation statements)

References 11 publications

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“…This research can help different radiologists make consistent judgement. Charan et al [21] used the CNN model to classify normal and abnormal mammograms. They segmented the breast area through morphological operations, which can effectively improve the classification performance of the model.…”

Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/ Malignancy) of two-view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.

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Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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“…Table 3 contains the values of overall Accuracy rate, Sensitivity and Specificity values of Proposed Method (LBP+CT). [22] and by comparing the previous research in mammogram classification [23] a comparative study shown in Figure 6.…”

Section: Experimentalresultsmentioning

confidence: 99%

Hybrid Method of Local Binary Pattern and Classiﬁcation Tree for Early Breast Cancer Detection by Mammogram Classiﬁcation

2019

IJRTE

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- Breast cancer is an alarming disease due to a mutation in breast cells and it is one type of cancer among women which highly leads to their death. One of the most effective tools for early detection of breast cancer is mammography, which is a screening tool used to examine the human breast by using low-dose amplitude X-rays. Computer-Aided Diagnosis (CAD) is used as an important tool to help the medical professionals for classifying breast tissues into a different class. Computer-Aided Diagnosis (CAD) can be used to reduce human error in reading the mammograms and it shows effective results in the classification of benign and malignant abnormalities. The proposed method presents a new classification approach to detect the abnormalities in mammograms using Local Binary Pattern and Decision Tree Classification. A Uniform Local Binary Pattern(uLBP) is an extension of the original Local Binary Pattern in which only patterns that contain at most two transitions from 0 to 1 (or vice versa) are considered. In uniform Local Binary Pattern (LBP) mapping, there is a separate output label for each uniform pattern and all then on uniform patterns are assigned to a single label. These patterns are utilized to detect breast cancer by classification employing the Decision Tree Classification. Specificity and sensitivity are the two statistical measures used in this proposed method to verify and measure the significance of the test related to abnormalities in the breast tissues. Thus, it can be a measurement of performance tests for classifying the patients who do and do not suffer from cancer. The mini-MIAS mammography database is employed for testing the accuracy of the proposed method and the results are promising.

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“…78,82,83 Recently deep learning algorithms such as CNNs have achieved breakthrough prediction power in a variety of medical studies, including detection of lung nodules on CT scans [84][85][86] and detection of breast cancer on mammograms. 87,88 A comparison in mortality prediction from chest CT between a deep learning framework and a standard framework with radiomics features showed increased accuracy with CNN-based classification. 89 Multitask learning is expected to help provide a degree of interpretation for deep learning approaches.…”

Section: Outcome Modeling By Machine Learningmentioning

confidence: 99%

“…89 Multitask learning is expected to help provide a degree of interpretation for deep learning approaches. 76,[81][82][83][84][85][86][87][88][89][90] Given enough high-quality data (text and images), it is expected that the role of CNNs will continue to expand in medicine and quantitative imaging. Despite these advances, however, concerted efforts are needed to promote detailed understanding of these approaches, including the relationship between dataset sizes, possible confounders, and performance of outcome prediction.…”

Section: Outcome Modeling By Machine Learningmentioning

confidence: 99%

NCTN Assessment on Current Applications of Radiomics in Oncology

Nie

Al‐Hallaq

et al. 2019

International Journal of Radiation Oncology*Biology*Physics

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Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In NotedAn online CME test for this article can be taken at https:// academy.astro.org.

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Breast cancer detection in mammograms using convolutional neural network

Cited by 111 publications

References 11 publications

New convolutional neural network model for screening and diagnosis of mammograms

New convolutional neural network model for screening and diagnosis of mammograms

Hybrid Method of Local Binary Pattern and Classiﬁcation Tree for Early Breast Cancer Detection by Mammogram Classiﬁcation

NCTN Assessment on Current Applications of Radiomics in Oncology

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