Location of mammograms ROI's and reduction of false-positive

Salazar-Licea, Luis. A.; Pedraza-Ortega, Jess Carlos; Palma, Alberto Pastrana; Aceves-Fernández, Marco Antonio

doi:10.1016/j.cmpb.2017.02.003

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…Several researchers have implemented clustering method like K-means and Fuzzy C-means (FCM) for breast abnormality segmentation [ 3 , 23 ]. However, they have limitations in terms of learning abilities.…”

Section: Literature Surveymentioning

confidence: 99%

“…This FP consumes much time of radiologists and results into unnecessary biopsies. Thus, reducing the FPs is an open research problem and various researchers have proposed FP reduction algorithms to improve the specificity of the CAD systems [ 5 , 9 , 23 , 26 – 31 ]. Usually, FP reduction algorithm is postprocessing step of a CAD system with two stages namely: Feature extraction and Classification.…”

Section: Literature Surveymentioning

confidence: 99%

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Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Pawar¹,

Talbar

Dudhane

2018

Journal of Healthcare Engineering

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Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Pawar¹,

Talbar

Dudhane

2018

Journal of Healthcare Engineering

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“…However, in clinical practice, the sensitivity of physician readers in the ECG diagnosis of hyperkalemia has been estimated to be as low as 34% to 43% . Deep learning is a type of artificial intelligence that uses representation methods to identify meaningful patterns from complex digital files and has been used in medicine to identify lesions in mammograms or retinal images . We hypothesized that a deep-learning model (DLM) could effectively rule out, or screen for, hyperkalemia.…”

Section: Introductionmentioning

confidence: 99%

“…10 Deep learning is a type of artificial intelligence that uses representation methods to identify meaningful patterns from complex digital files and has been used in medicine to identify lesions in mammograms or retinal images. 11,12 We hypothesized that a deep-learning model (DLM) could effectively rule out, or screen for, hyperkalemia. To test this hypothesis, we trained and validated a DLM to classify hyperkalemia from ECGs in patients with CKD.…”

mentioning

confidence: 99%

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Galloway¹,

Valys²,

Shreibati³

et al. 2019

JAMA Cardiol

233

115

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IMPORTANCEFor patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.OBJECTIVE To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. DESIGN, SETTING, AND PARTICIPANTSA deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018.EXPOSURES Use of a deep-learning model. MAIN OUTCOMES AND MEASURESArea under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity. RESULTSOf the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona.CONCLUSIONS AND RELEVANCE In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.

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“…Then algorithm for segmentation, based on intensity value, which are discontinuous, are also proposed by Jasmeen Kaur and MandeepKaur [13]. In 2017 by Luis Antonio Salazar-Licea et al [15] proposed a method for locating ROI using combined Shi-Tomasi corner detection, image thresholding and SIFT descriptors to improve the accuracy rates. However the literature presented provides some solution for the detection of breast cancer, yet it is still a challenging problem because the detection completely dependent on locating doubtful regions without missing any information.…”

Section: Introductionmentioning

confidence: 99%

Novel approach to locate region of interest in mammograms for Breast cancer

Divyashree¹,

Amarnath²,

Naveen³

et al. 2018

ijisae

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Locating region of interest for breast cancer masses in the mammographic image is a challenging problem in medical image processing. In this research work, the keen idea is to efficiently extract suspected mass region for further examination. In particular to this fact breast boundary segmentation on sliced rgb image using modified intensity based approach followed by quad tree based division to spot out suspicious area are proposed in the paper. To evaluate the performance DDSM standard dataset are experimented and achieved acceptable accuracy.

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Location of mammograms ROI's and reduction of false-positive

Cited by 13 publications

References 21 publications

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Novel approach to locate region of interest in mammograms for Breast cancer

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