A knowledge-based approach to the CADx of mammographic masses

Elter, Matthias; Haßlmeyer, Erik

doi:10.1117/12.770135

Cited by 10 publications

(5 citation statements)

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“…Some modules of CBMIR architecture are also present in most of CAD definitions [27,17]. While the primary goal of the CBMIR tool is to retrieve medical images regarding content [24,7], CAD applications are commonly designed to perform image classification [4,37,28]. Table 1 summarizes the state-of-the-art CAD and CBMIR applied over mammograms.…”

Section: Combining Cad and Cbmir For Analyses Of Mammogramsmentioning

confidence: 99%

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Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

et al. 2015

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Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

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Section: Combining Cad and Cbmir For Analyses Of Mammogramsmentioning

confidence: 99%

“…The work of Elter [28] combines content and clinical data to calculated weights used in a classification according to decision rules. Although the approach achieved a high ROC curve value, it employed only a few elements of DDSM.…”

Section: Combining Cad and Cbmir For Analyses Of Mammogramsmentioning

confidence: 99%

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

et al. 2015

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“…Elter and Halmeyer [6] processed classification under the application of Artificial Neural Network (ANN) and Euclidean metric classification, correspondingly, and attained a performance to greater extent. The developers have applied 2-class classification; however 2-class classification is insufficient to eliminate unwanted biopsy as in abnormal cases the tumor might be benign or malignant.…”

Section: Introductionmentioning

confidence: 99%

Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

Priya¹

2021

TURCOMAT

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Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.

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“…Abirami et al [ 7 ] used wavelet features for the two-class classification of digital mammograms; they have achieved 93% accuracy on MIAS data set. Elter and Halmeyer [ 8 ] performed classification using Artificial Neural Network (ANN) and Euclidean metric classifier, respectively, and achieved a performance over 85%. All of the above researchers used two-class classification but two-class classification is not enough to avoid unnecessary biopsy because in abnormal cases the tumor can be either benign or malignant.…”

Section: Introductionmentioning

confidence: 99%

Three-Class Mammogram Classification Based on Descriptive CNN Features

Jadoon

Zhang

Haq

et al. 2017

BioMed Research International

132

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In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

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A knowledge-based approach to the CADx of mammographic masses

Cited by 10 publications

References 0 publications

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

Three-Class Mammogram Classification Based on Descriptive CNN Features

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