Fully automated scheme for computer‐aided detection and breast cancer diagnosis using digitised mammograms

Eltrass, Ahmed S.; Salama, Mohamed S.

doi:10.1049/iet-ipr.2018.5953

Cited by 33 publications

(16 citation statements)

References 44 publications

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“…The learning rate of ADAGrad is a variable parameter, depending on how frequently it is updated [ 21 ]. This learning rate and initial accumulator value of ADAGrad were tuned as 0.0001 and 0.1, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…In total, 1547, 326, and 200 ROI images of the CBIS–DDSM dataset were used for training, testing, and validation, respectively. In order to tune the network parameters in an automated manner without the intervention of an operator, various optimizers were applied in this study—namely, ADAM, ADAGrad, ADADelta, and RMSProp [ 21 , 22 ]. Adam is a replacement optimization algorithm for stochastic gradient descent used in trained deep learning models.…”

Section: Methodsmentioning

confidence: 99%

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Deep Learning Capabilities for the Categorization of Microcalcification

Singh

Kumar

Antonakakis

et al. 2022

IJERPH

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Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed deep learning using convolution neural networks (CNNs) for the automatic detection and classification of all three categories of microcalcification. CNN was applied using four different optimizers (ADAM, ADAGrad, ADADelta, and RMSProp). The input images of a size of 299 × 299 × 3, with fully connected RELU and SoftMax output activation functions, were utilized in this study. The feature map was obtained using the pretrained InceptionResNetV2 model. The performance evaluation of our classification scheme was tested on a curated breast imaging subset of the DDSM mammogram dataset (CBIS–DDSM), and the results were expressed in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). Our proposed classification scheme outperforms the ability of previously used deep learning approaches and classical machine learning schemes.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Deep Learning Capabilities for the Categorization of Microcalcification

Singh

Kumar

Antonakakis

et al. 2022

IJERPH

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“…Different pre-trained CNNs [41][42][43] were trained on a huge dataset, namely the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Various ML techniques and pre-trained CNN architectures were investigated for medical imaging classification [44][45][46]. In contrast, few DL models have reported robust performance for ECG classification of cardiac diseases [47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Eltrass

Tayel

Ammar

2022

Neural Comput & Applic

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Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.

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“…With the practical challenges that breast tumors offer due to their variation in size, shape, location, and texture, there has been a signi cant need to improve the overall performance of CAD systems and reduce false positive and negative cases. With the recent progress in computers and their enhanced computational capacity and speed, deep learning methodology has been broadly suggested in biomedical applications and particularly in CAD systems for mammography [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

Baccouche

Garcia‐Zapirain

Elmaghraby

2022

Preprint

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A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially in one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for 1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; 2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and 3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies.

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Fully automated scheme for computer‐aided detection and breast cancer diagnosis using digitised mammograms

Cited by 33 publications

References 44 publications

Deep Learning Capabilities for the Categorization of Microcalcification

Deep Learning Capabilities for the Categorization of Microcalcification

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

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