Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound

Prabusankarlal, K.M.; Thirumoorthy, P.; Manavalan, R.

doi:10.1186/s13673-015-0029-y

Cited by 55 publications

(41 citation statements)

References 42 publications

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“…Additionally, we would like to experiment with different texture features that we did not include in this study (eg, local binary patterns) to see whether a more refined feature set can improve the classification performance of an SVM model. Along similar lines, this work was only evaluated by using texture features, whereas an investigation using both morphologic and texture image characteristics would be ideal …”

Section: Discussionmentioning

confidence: 99%

Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case‐Control Study

Behr

Noseworthy

Kumbhare

2019

J of Ultrasound Medicine

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Objectives Myofascial pain syndrome (MPS) is the most common cause of chronic pain worldwide. The diagnosis of MPS is subjective, which has created a need for a robust quantitative method of diagnosing MPS. We propose that using a support vector machine (SVM) along with ultrasound (US) texture features can differentiate between healthy and MPS‐affected skeletal muscle. Methods B‐mode US video data were collected in the upper trapezius muscle of healthy (29) participants and patients with active (21) and latent (19) MPS, using an acquisition method outlined in previous works. Regions of interest were extracted and filtered to obtain a unique set of 917 images where texture features were extracted from each region of interest to characterize each image. These texture features were then used to train 4 separate binary SVM classifiers using nested cross‐validation to implement feature selection and hyperparameter tuning. The performance of each kernel was estimated on the data and validated through testing on a final holdout set. Results The radial basis function kernel classifier had the greatest Matthews correlation coefficient performance estimate of 0.627 ± 0.073 (mean ± SD) along with the largest area under the curve of 91.0% ± 3.0%. The final holdout test for the radial basis function classifier resulted in 86.96 accuracy, a Matthews correlation coefficient of 0.724, 88% sensitivity, and 86% specificity, validating our earlier performance estimates. Conclusions We have demonstrated that specific US texture features that have been used in other computer‐aided diagnostic literature are feasible to use for the classification of healthy and MPS muscle using a binary SVM classifier.

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Section: Discussionmentioning

confidence: 99%

Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case‐Control Study

Behr

Noseworthy

Kumbhare

2019

J of Ultrasound Medicine

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“…We used a support vector machine (SVM) for shape pattern classification [10][11][12]. Figure 13 shows the grade classification method based on pattern recognition.…”

Section: The Results Of Shape Analysismentioning

confidence: 99%

Development of an automatic sorting system for fresh ginsengs by image processing techniques

Jeong

Lee

2017

Hum. Cent. Comput. Inf. Sci.

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This study was conducted with the objective of implementing a smart IoT (internet of things) factory consisting of an automatic 6-year-old fresh ginseng grade classification device. Conventionally, washed 6-year-old ginseng from farmlands is manually sorted into three grades using classification criteria such as weight and shape. However, the cost associated with this classification process has been on the increase. Consequently, to reduce this associated cost, we developed an automatic ginseng sorting device that classifies 6-year-old ginseng according to weight and shape via image processing and sends the classification results to a factory server over a network. Evaluations conducted of the performance of the developed machine using 100 units of 6-year-old ginseng showed that it has a high recognition rate, with an accuracy of 94% for Grade 1, 98% for Grade 2, and 90% for Grade 3.

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“…To measure the performance of our proposed approach, we applied the 10-fold cross-validation technique [26] for the classification experiments. It increases the evaluation reliability of the classifier.…”

Section: Performance Measurement Methodologymentioning

confidence: 99%

XSSClassifier: An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs

Rathore¹,

Sharma²,

Park³

2017

J Inf Process Syst

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Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as crosssite scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learningbased approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

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Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound

Cited by 55 publications

References 42 publications

Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case‐Control Study

Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case‐Control Study

Development of an automatic sorting system for fresh ginsengs by image processing techniques

XSSClassifier: An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs

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