DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features

Abbas, Qaisar

doi:10.3390/computers5040028

Cited by 49 publications

(41 citation statements)

References 44 publications

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“…The obtained results are reported in Tables 2 and 3. Table 3 results have been taken from paper [17] for DeepCAD, CNN-MAX-CAD-Qiu and CNN-CAD-Jiao and for Ball and Varela has been taken from [18]. Ball shows 87% accuracy on DDMS dataset, Varela shows 81% on DDMS dataset.…”

Section: Resultsmentioning

confidence: 99%

“…Proposed method has been compared with state-of-the-art methods that used the same concept like deep learning classification algorithms. The most common method used in this area are DeepCAD [17], CNN-Max-CAD-Qiu [15] and CNN-CAD-Jiao [16] by using same parameters and datasets. Table 2 shows results of the proposed method on different datasets like MIAS-mini, DDMS, combination set of MIASmini and DDMS and combination of MIAS-mini, DDMS and artificially generated datasets.…”

Section: Resultsmentioning

confidence: 99%

“…Again there is no rule that which features are most suitable. Classification has been performed by using some old classifiers like artificial neural network, KNN, Bayesian [17], [18]. But most of the times, these features extraction and classifiers are not suitable.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning based Computer Aided Diagnosis System for Breast Mammograms

Arfan¹

2017

ijacsa

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Abstract-In this paper, a framework has been presented by using a combination of deep Convolutional Neural Network (CNN) with Support Vector Machine (SVM). Proposed method first perform preprocessing to resize the image so that it can be suitable for CNN and perform enhancement quality of the images can be enhanced. Deep Convolutional Neural Network (CNN) has been used for features extraction and classification with Support Vector Machine (SVM). Standard dataset MIAS and DDMS has been employed for testing the proposed framework by generating new images from these datasets by the process of augmentation. Different performance measures like Accuracy, Sensitivity, Specificity and area under the curve (AUC) has been employed as a quantitative measure and compared with state of the art existing methods. Results shows that proposed framework has attained accuracy 93.35% and 93% sensitivity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Deep Learning based Computer Aided Diagnosis System for Breast Mammograms

Arfan¹

2017

ijacsa

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“…For different pictures of a similar breast, the average scores of individual pictures is taken. This methodology was spurred by a past report on autonomous human perusers (readers), and it has demonstrated sensibly powerful, while being both basic and adaptable [14]. per lesion methodology.…”

Section: Methodology 1 Faster R-cnnmentioning

confidence: 99%

“…(d) DDSM database:The images of DDSM [9,10,11,32,14] are usually saved in a non-standard compressed document that need utilization of decompressing codes. Besides, the annotations of ROI for the variations in the images of DDSM demonstrate general location of injuries/lesions, without exact division of them.…”

Section: Digital Repositories Of Breast Cancermentioning

confidence: 99%

Scrutiny of Breast Cancer Detection Techniques of Deeplearning and Machine Learning

2019

IJRTE

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Breast cancer is one of the most widely recognized tumors globally among ladies with the data available that one of every eight ladies is influenced by this illness during their lifetime. Mammography is the best imaging methodology for early location of the disease in beginning times. On account of poor complexity and low perceivability in the mammographic pictures, early discovery of the cancer malignant growth is a huge challenge to effective cure of the disease. Distinctive CAD (computer aided detection) supported algorithms have been developed to enable radiologists to give an exact determination. This paper highlights the study of the most widely recognized methodologies of image segmentation created for recognition of calcifications and masses. The principle focal point of this survey is on picture theof strategies and the factors utilized for early bosom disease identification. Surface investigation is the vital advance in any picture division strategies of image segmentation which depend on a nearby spatial variety of color or shading. Subsequently, different techniques for texture investigation for small scale calcification and mass identification in mammography are talked about in the mechanism of mammography. The point of this paper is to audit existing ways to deal with the segmentation of masses and automated detection in mammographic pictures, underlining the key-focuses and primary contrasts among the utilized systems. The key goal is to bring up the preferences and drawbacks of the different methodologies. Conversely with different surveys which just portray and think about various methodologies subjectively, this audit likewise gives a quantifiable examination.In proposed research use deep learning base network for classification of mammography images . In previous approaches use machine learning base learning. The Main drawback of machine learning is selection of features manualy or by functions but in deep learning automatic feature detect and its vary according to image. The demonstration of seven mass recognition techniques is thought about utilizing two distinctive databases of mammography: an open digitized database and a full-field (local) advanced digitized database. The outcomes are given as far as Free reaction Receiver Operating Characteristic (FROC) and Receiver Operating Characteristic (ROC) examination.

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Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms

et al. 2022

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Purpose Automatic detection of very small and nonmass abnormalities from mammogram images has remained challenging. In clinical practice for each patient, radiologists commonly not only screen the mammogram images obtained during the examination, but also compare them with previous mammogram images to make a clinical decision. To design an artificial intelligence (AI) system to mimic radiologists for better cancer detection, in this work we proposed an end‐to‐end enhanced Siamese convolutional neural network to detect breast cancer using previous year and current year mammogram images. Methods The proposed Siamese‐based network uses high‐resolution mammogram images and fuses features of pairs of previous year and current year mammogram images to predict cancer probabilities. The proposed approach is developed based on the concept of one‐shot learning that learns the abnormal differences between current and prior images instead of abnormal objects, and as a result can perform better with small sample size data sets. We developed two variants of the proposed network. In the first model, to fuse the features of current and previous images, we designed an enhanced distance learning network that considers not only the overall distance, but also the pixel‐wise distances between the features. In the other model, we concatenated the features of current and previous images to fuse them. Results We compared the performance of the proposed models with those of some baseline models that use current images only (ResNet and VGG) and also use current and prior images (long short‐term memory [LSTM] and vanilla Siamese) in terms of accuracy, sensitivity, precision, F1 score, and area under the curve (AUC). Results show that the proposed models outperform the baseline models and the proposed model with the distance learning network performs the best (accuracy: 0.92, sensitivity: 0.93, precision: 0.91, specificity: 0.91, F1: 0.92 and AUC: 0.95). Conclusions Integrating prior mammogram images improves automatic cancer classification, specially for very small and nonmass abnormalities. For classification models that integrate current and prior mammogram images, using an enhanced and effective distance learning network can advance the performance of the models.

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DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features

Cited by 49 publications

References 44 publications

Deep Learning based Computer Aided Diagnosis System for Breast Mammograms

Deep Learning based Computer Aided Diagnosis System for Breast Mammograms

Scrutiny of Breast Cancer Detection Techniques of Deeplearning and Machine Learning

Feature fusion Siamese network for breast cancer detection comparing current and prior mammograms

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