Deep learning and non-negative matrix factorization in recognition of mammograms

Świderski, Bartosz; Kurek, Jarosław; Osowski, S.; Kruk, Michał; Barhoumi, Walid

doi:10.1117/12.2266335

Cited by 23 publications

(19 citation statements)

References 6 publications

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“…Suzuki et al (2016) implemented CNNs for mass detection in mammographic images in computer-aided diagnosis [52]. Spanhol et al (2016) applied CNNs for the classification of breast cancer from histopathological images [53], whereas Wichakam and Vateekul, (2016) combined deep convolutional networks and SVMs for mass detection on digital mammograms [54]. Swiderski et al investigated the application of deep learning and non-negative matrix factorization for breast cancer recognition in mammograms [55].…”

Section: Related Research In Breast Cancer Diagnosis Using Convolutiomentioning

confidence: 99%

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

et al. 2020

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(1) Background: Bone metastasis is one of the most frequent diseases in breast, lung and prostate cancer; bone scintigraphy is the primary imaging method of screening that offers the highest sensitivity (95%) regarding metastases. To address the considerable problem of bone metastasis diagnosis, focused on breast cancer patients, artificial intelligence methods devoted to deep-learning algorithms for medical image analysis are investigated in this research work; (2) Methods: Deep learning is a powerful algorithm for automatic classification and diagnosis of medical images whereas its implementation is achieved by the use of convolutional neural networks (CNNs). The purpose of this study is to build a robust CNN model that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer; (3) Results: A robust CNN architecture is selected based on CNN exploration performance for bone metastasis diagnosis using whole-body scan images, achieving a high classification accuracy of 92.50%. The best-performing CNN method is compared with other popular and well-known CNN architectures for medical imaging like ResNet50, VGG16, MobileNet, and DenseNet, reported in the literature, providing superior classification accuracy; and (4) Conclusions: Prediction results show the efficacy of the proposed deep learning approach in bone metastasis diagnosis for breast cancer patients in nuclear medicine.

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Section: Related Research In Breast Cancer Diagnosis Using Convolutiomentioning

confidence: 99%

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

et al. 2020

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“…Six of these studies provided the source of data [49,50,52,54,85] while nine studies did not publish the source of data [90,91,92,93,95,101,102,104]. Two research studies used mammographs for detection along with CNN and published data source [51,53]. Eight studies [12,59,77,87,96,98,103,105] used CT Slices, three of which used data from PROMISE [75], and LIDC [166].…”

Section: Discussionmentioning

confidence: 99%

“…When the training data is limited to a few patterns over-fitting can occur. To overcome it, Swiderski et al presented a way to enrich the training data using a non-negative matrix factorization (NMF) and statistical self-similarity [53]. Ertosun et al presented a model which at first detects the presence of the mass in the mammograms and then it locates the mass from the mammographic images [51].…”

Section: Models and Algorithmsmentioning

confidence: 99%

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Munir

Elahi

Ayub

et al. 2019

Cancers

321

182

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In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements.

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“…Features are characteristics of the ROI taken from the shape and margin of lesions, masses, and calcifications. These features can be categorized into texture and morphologic features [12,86], descriptors, and model-based features [52], which help to discriminate between benign and malignant lesions. Most of the texture features are calculated from the entire image or ROIs using the gray-level value and the morphological features.…”

Section: Postprocessing Image Feature Extraction and Selectionmentioning

confidence: 99%

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020

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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

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Deep learning and non-negative matrix factorization in recognition of mammograms

Cited by 23 publications

References 6 publications

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

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