Deep Convolutional Neural Networks for breast cancer screening

Chougrad, Hiba; Zouaki, Hamid; Alheyane, Omar

doi:10.1016/j.cmpb.2018.01.011

Cited by 386 publications

(252 citation statements)

References 20 publications

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“…Recent advances in machine learning and image processing have culminated in the emergence of computer-aided diagnosis (CAD) systems which have been often used as an additional and useful tool to help doctors make final diagnostic decisions and act as a second opinion. Recently, a broad range of CAD systems have been proposed and achieved remarkable performance to predict breast cancer from mammography images [9][10][11][12][13][14][15][16][17]. However, most of these works focused on the classification of the detected breast abnormalities as either benign or malignant (i.e., pathology classes).…”

Section: Introductionmentioning

confidence: 99%

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Boumaraf

Liu

Ferkous

et al. 2020

BioMed Research International

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Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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Section: Introductionmentioning

confidence: 99%

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Boumaraf

Liu

Ferkous

et al. 2020

BioMed Research International

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“…Differently to transfer learning as a new ConvNet, some of the last layers of the model are retrained with the new data as indicated in Section 2.3. For example, VGG16 [39], InceptionV3 [20], and ResNet50 [18] are fine tuned in [44]; the author found that when the number of convolutional blocks exceeds 2, the accuracy of the fine tuned model drops. Also, a comparison of the classification performance between the training of VGG16 in FT and using it as a feature extractor is explored in [45].…”

Section: Fine Tuningmentioning

confidence: 99%

“…However, the rotation operation yields to distortion of the original image. Because of this reason, right angle rotations are preferred to random rotation angles [37,43,44].…”

Section: Data Augmentation and Pre-processingmentioning

confidence: 99%

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Falconi¹,

Pérez²,

Aguila³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Breast cancer has an important incidence in women mortality worldwide. Currently, mammography is considered the gold standard for breast abnormalities screening examinations, since it aids in the early detection and diagnosis of the illness. However, both identification of mass lesions and its malignancy classification is a challenging problem for artificial intelligence. In this work, we extend our previous research in mammogram classification, where we studied NasNet and MobileNet in transfer learning to train a breast abnormality malignancy classifier, and include models like: VGG, Resnet, Xception and Resnext. However, training deep learning models tends to overfit. This problem is also carried out in this work. Our results show that Fine Tuning achieves the best classifier performance in VGG16 with AUC value of 0.844 in the CBIS-DDSM dataset.

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“…One potential promising approach is the deep learning as shown in [4]. In [6], a deep CNN is proposed using transfer learning. A set of Region-of-Interests are extracted from the mammograms and normalized before feed to the network.…”

Section: Related Workmentioning

confidence: 99%

“…Convolutional neural networks in particular leads to a remarkable impact in image analysis and understanding especially in image segmentation, classification and analysis [4]. Several models employ deep learning are already developed for diagnosis and identification of breast cancer through analysis of digital mammography [5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for breast cancer diagnosis

Rashed

Seoud

2019

Proceedings of the 2019 8th International Conference on Software and Information Engineering

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Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense breast structure produced due to the compression process during imaging lead to difficulties to recognize small size abnormalities. Also, inter-and intra-variations of breast tissues lead to significant difficulties to achieve high diagnosis accuracy using hand-crafted features. Deep learning is an emerging machine learning technology that requires a relatively high computation power. Yet, it proved to be very effective in several difficult tasks that requires decision making at the level of human intelligence. In this paper, we develop a new network architecture inspired by the U-net structure that can be used for effective and early detection of breast cancer. Results indicate a high rate of sensitivity and specificity that indicate potential usefulness of the proposed approach in clinical use.

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Deep Convolutional Neural Networks for breast cancer screening

Cited by 386 publications

References 20 publications

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Deep learning approach for breast cancer diagnosis

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