Computer-aided diagnosis in digital mammography: comparison of two commercial systems

Fauci, F.; Raso, G.; Cascio, Donato; Iacomi, M.; Ienzi, R.; Vasile, Maria Simone; Castrogiovanni, Debora; Filosto, Guido

doi:10.2217/iim.13.68

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…Many methods have been proposed to achieve a robust mammography-based CAD system for microcalcification diagnosis [13][14][15][16][17][18], in some cases well performing in dealing with specific abnormalities. Nevertheless, the automatic and accurate classification of microcalcification clusters, especially in differentiating the benign from the malignant ones, remains still complicated due to their nature.…”

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confidence: 99%

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

et al. 2020

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Background: Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results: For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions: The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

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confidence: 99%

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

et al. 2020

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“…From the comparison of the system proposed here with the other method [9] of using the same database, it is easy to deduce the quality of the analysis carried out and the potentialities of the method.…”

Section: Resultsmentioning

confidence: 99%

“…The development of a classification system is intimately linked to the database used [8,9]. In this work, a public dataset (provided by AIDA: AutoImmunité, Diagnostic Assisté par ordinateur project) was used [5].…”

Section: Databasementioning

confidence: 99%

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

2019

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Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.

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“…Computer Aided Diagnosis (CAD) systems have been widely proposed in different areas of medicine and with different objectives, such as second-reading, improving the speed of the diagnostic processes, training physicians for special tasks, etc. [6,7]. In recent years, the diagnostic support of CAD systems has been proposed for an automatic human epithelial type 2 (HEp-2) images classification.…”

Section: Introductionmentioning

confidence: 99%

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

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The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.

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Computer-aided diagnosis in digital mammography: comparison of two commercial systems

Cited by 12 publications

References 18 publications

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

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