Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

Kamra, Amit; Jain, Vibha; Singh, Sukhwinder; Mittal, Sunil

doi:10.1007/s10278-015-9807-3

Cited by 28 publications

(16 citation statements)

References 21 publications

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“…There were 23 eligible studies based on the literature search strategy (additional details and excluded studies [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] shown in Appendix 1). A summary of the eligible studies, including study characteristics, is shown in Table 1; and study findings are reported in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Houssami

Kirkpatrick-Jones

Noguchi

et al. 2019

Expert Review of Medical Devices

126

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Introduction: Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale use in population-based screening. Areas covered: We performed a scoping review, a structured evidence synthesis describing a broad research field, to summarize knowledge on AI evaluated for BC detection and to assess AI's readiness for adoption in BC screening. Studies were predominantly small retrospective studies based on highly selected image datasets that contained a high proportion of cancers (median BC proportion in datasets 26.5%), and used heterogeneous techniques to develop AI models; the range of estimated AUC (area under ROC curve) for AI models was 69.2-97.8% (median AUC 88.2%). We identified various methodologic limitations including use of non-representative imaging data for model training, limited validation in external datasets, potential bias in training data, and few comparative data for AI versus radiologists' interpretation of mammography screening. Expert opinion: Although contemporary AI models have reported generally good accuracy for BC detection, methodological concerns, and evidence gaps exist that limit translation into clinical BC screening settings. These should be addressed in parallel to advancing AI techniques to render AI transferable to large-scale population-based screening.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Houssami

Kirkpatrick-Jones

Noguchi

et al. 2019

Expert Review of Medical Devices

126

View full text Add to dashboard Cite

show abstract

“… Zhang et al 25 BM 94.85% 78.20 – 8. Kamra et al 26 BM 71.43 97.22 93.02 9. Rouhie et al 27 BM 85.41 91.89 88.65 10.…”

Section: System Testing and Performance Evaluationmentioning

confidence: 99%

<p>An automated mammogram classification system using modified support vector machine</p>

Kayode

Oluwatobi

Adegun

et al. 2019

MDER

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Purpose Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer diagnosis process; hence this study. Methodology The inputs to the proposed model are raw mammograms downloaded from the Mammographic Image Analysis Society database. Prior to the classification, the raw mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract fifteen textural features from the mammograms at four different angular directions: θ={0°, 45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector machine was used to classify the mammograms as normal, benign and malignant. Results All of the 37 normal images used as test data were classified as normal (no false positive) and all 41 abnormal images were correctly classified to be abnormal (no false negative), meaning that the sensitivity and specificity of the model in detecting abnormality is 100%. After the detection of abnormality, the system further classified the abnormality on the mammograms to be either “benign” or “malignant”. Out of 23 benign images, 21 were truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be malignant. From these findings, the sensitivity, specificity, positive predictive value, and negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively. Conclusion This article has further affirmed the prowess of automated CADx systems as a viable tool that could facilitate breast cancer diagnosis by radiologists.

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“…Most current medical image edge detection techniques can be categorized as feature-classify approaches [2][3][4][5], threshold segmentation approaches [6,7], and contour curve detection approaches [8][9][10][11]. Tang et al [12] have developed a splat feature classification method to detect retinal hemorrhage.…”

Section: Introductionmentioning

confidence: 99%

Detection of Lung Contour with Closed Principal Curve and Machine Learning

Peng

Wang

et al. 2018

J Digit Imaging

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Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.

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Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

Cited by 28 publications

References 21 publications

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

<p>An automated mammogram classification system using modified support vector machine</p>

Detection of Lung Contour with Closed Principal Curve and Machine Learning

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