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

BACKGROUND Computer-aided diagnosis (CAD) is a useful tool that can provide a reference for the differential diagnosis of benign and malignant breast lesion. Previous studies have demonstrated that CAD can improve the diagnostic performance. However, conventional ultrasound (US) combined with CAD were used to adjust the classification of category 4 lesions has been few assessed. OBJECTIVE The objective of our study was to evaluate the diagnosis performance of conventional ultrasound combined with a CAD system S-Detect in the category of BI-RADS 4 breast lesions. METHODS Between December 2018 and May 2020, we enrolled patients in this study who received conventional ultrasound and S-Detect before US-guided biopsy or surgical excision. The diagnostic performance was compared between US findings only and the combined use of US findings with S-Detect, which were correlated with pathology results. RESULTS A total of 98 patients (mean age 51.06 ±16.25 years, range 22-81) with 110 breast masses (mean size1.97±1.38cm, range0.6-8.5) were included in this study. Of the 110 breast masses, 64/110 (58.18%) were benign, 46/110 (41.82%) were malignant. Compared with conventional ultrasound, a significant increase in specificity (0% to 53.12%, P<.001), accuracy (41.81% to70.19%, P<.001) were noted, with no statistically significant decrease on sensitivity(100% to 95.65% ,P=.48). According to S-Detect-guided US BI-RADS re-classification, 30 out of 110 (27.27%) breast lesions underwent a correct change in clinical management, 74of 110 (67.27%) breast lesions underwent no change and 6 of 110 (5.45%) breast lesions underwent an incorrect change in clinical management. The biopsy rate decreased from 100% to 67.27 % (P<.001).Benign masses among subcategory 4a had higher rates of possibly benign assessment on S-Detect for the US only (60% to 0%, P<.001). CONCLUSIONS S-Detect can be used as an additional diagnostic tool to improve the specificity and accuracy in clinical practice. S-Detect have the potential to be used in downgrading benign masses misclassified as BI-RADS category 4 on US by radiologist, and may reduce unnecessary breast biopsy. CLINICALTRIAL none

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Discrimination of benign and malignant solid breast masses using deep residual learning-based bimodal computer-aided diagnosis system

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.103453 ◽

2022 ◽

Vol 73 ◽

pp. 103453

Author(s):

Zahra Assari ◽

Ali Mahloojifar ◽

Nasrin Ahmadinejad

Keyword(s):

Computer Aided Diagnosis ◽

Breast Masses ◽

Diagnosis System ◽

Residual Learning ◽

Computer Aided ◽

Aided Diagnosis

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Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

Annals of Biomedical Engineering ◽

10.1007/s10439-018-2044-4 ◽

2018 ◽

Vol 46 (9) ◽

pp. 1419-1431 ◽

Cited By ~ 12

Author(s):

Gopichandh Danala ◽

Bhavika Patel ◽

Faranak Aghaei ◽

Morteza Heidari ◽

Jing Li ◽

...

Keyword(s):

Computer Aided Diagnosis ◽

Breast Masses ◽

Contrast Enhanced ◽

Computer Aided ◽

Aided Diagnosis

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A computer-aided diagnosis system for prediction of the probability of malignancy of breast masses on ultrasound images

10.1117/12.813722 ◽

2009 ◽

Cited By ~ 2

Author(s):

Jing Cui ◽

Berkman Sahiner ◽

Heang-ping Chan ◽

Jiazheng Shi ◽

Alexis Nees ◽

...

Keyword(s):

Computer Aided Diagnosis ◽

Ultrasound Images ◽

Breast Masses ◽

Diagnosis System ◽

Computer Aided ◽

Aided Diagnosis

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Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition

Technology in Cancer Research & Treatment ◽

10.1177/1533033819830748 ◽

2019 ◽

Vol 18 ◽

pp. 153303381983074 ◽

Cited By ~ 7

Author(s):

Antonin Prochazka ◽

Sumeet Gulati ◽

Stepan Holinka ◽

Daniel Smutek

Keyword(s):

Support Vector Machine ◽

Ultrasound Imaging ◽

Random Forests ◽

Thyroid Nodules ◽

Computer Aided Diagnosis ◽

Support Vector ◽

Computer Aided ◽

Binary Decomposition ◽

Independent Features ◽

Aided Diagnosis

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

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Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2020.103656 ◽

2021 ◽

Vol 113 ◽

pp. 103656

Author(s):

Rebecca Sawyer Lee ◽

Jared A. Dunnmon ◽

Ann He ◽

Siyi Tang ◽

Christopher Ré ◽

...

Keyword(s):

Computer Aided Diagnosis ◽

Breast Masses ◽

Computer Aided ◽

Aided Diagnosis

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A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

BioMed Research International ◽

10.1155/2020/7695207 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Said Boumaraf ◽

Xiabi Liu ◽

Chokri Ferkous ◽

Xiaohong Ma

Keyword(s):

Feature Selection ◽

Predictive Value ◽

Breast Imaging ◽

Feature Selection Method ◽

Computer Aided Diagnosis ◽

Screening Mammography ◽

Breast Masses ◽

Computer Aided ◽

Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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