Comparison of Naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

Cary, Theodore W.; Cwanger, Alyssa; Venkatesh, Santosh S.; Conant, Emily F.; Sehgal, Chandra M.

doi:10.1117/12.911916

Cited by 5 publications

(9 citation statements)

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“…The class corresponding to the highest posterior probability is the predicted class. Logistic regression (LR) [36,37] is a kind of nonlinear regression based on sigmoid function. Logistic regression defines the odds of an event as the ratio of the probability of occurrence to that of nonoccurrence.…”

Section: Classification Schemementioning

confidence: 99%

Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Yang

Zhao

et al. 2019

Journal of Sensors

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Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

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Section: Classification Schemementioning

confidence: 99%

Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Yang

Zhao

et al. 2019

Journal of Sensors

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“…Training and testing was performed by using Bayes model in which the probability of an event (malignancy) is revised based on the accumulation of new evidence (detection of sonographic features). Bayes probability of malignancy in the presence of sonographic features ( ) P M F was determined by the approach described earlier [12]. In short, it was determined by multiplying initial estimate of probability ( ) P M with the probabilities that feature F i is present in the malignant mass ( ) The statistical difference between the diagnostic performances of the three observations was determined based on p-values [13].…”

Section: Computer-aided Analysismentioning

confidence: 99%

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Sultan

Bouzghar

Levenback

et al. 2015

ABCR

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Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔA z , between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic per-* Corresponding author. L. R. Sultan et al. 2 formance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.

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“…There is considerable ongoing effort to improve diagnostic performance of imaging methods to reduce the unnecessary biopsies. Several research groups have proposed computer-based analysis of breast ultrasound images to improve differentiation between malignant and benign masses [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In our previous studies [14][15][16][17] we identified quantitative sonographic features from computer image analysis that were statistically different for benign and malignant breast masses.…”

Section: Introductionmentioning

confidence: 96%

“…In our previous studies [14][15][16][17] we identified quantitative sonographic features from computer image analysis that were statistically different for benign and malignant breast masses. These features along with the age of the patients were good predictors of malignancy [18][19]. In this study we expand the feature space to include mammographic BI-RADS category.…”

Section: Introductionmentioning

confidence: 98%

Combined Naïve Bayes and logistic regression for quantitative breast sonography

Sehgal

Cary

Cwanger

et al. 2012

2012 IEEE International Ultrasonics Symposium

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Sonography is commonly used as an adjunct to mammography for early detection of breast cancer. We are developing methods to classify solid breast masses in sonograms as malignant or benign. The goal of this study was to combine two independent probabilistic classifiers to improve computer-aided diagnosis of breast masses. Naïve Bayes and logistic regression were used for supervised classification of masses from extracted morphological sonographic features, in combination with mammographic BI-RADS (categories 1 to 5) and patient age. Solid masses with biopsy-proven diagnoses were analyzed. Training and testing were performed using leave-one-out cross validation. Diagnostic performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC). Agreement between predictions from the two classifiers was used to differentiate benign and malignant masses. The resultsshow that logistic regression and Naïve Bayes performed with ROC area of 0.902 ± 0.023 and 0.865 ± 0.027, respectively. The combined use of logistic regression and Naïve Bayes demonstrated reduction in biopsies by 48%, with malignancy missed in 2% of cases (false negative rate of 6.4%).Index Terms -quantitative breast ultrasound; computer-aided diagnosis; breast cancer; machine learning.

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Comparison of Naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

Cited by 5 publications

References 0 publications

Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Combined Naïve Bayes and logistic regression for quantitative breast sonography

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Comparison of Naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

Cited by 5 publications

References 0 publications

Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Combined Na&#x00EF;ve Bayes and logistic regression for quantitative breast sonography

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Combined Naïve Bayes and logistic regression for quantitative breast sonography