Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone

Rampun, Andrik; Zheng, Ling; Malcolm, Paul; Tiddeman, Bernard; Zwiggelaar, Reyer

doi:10.1088/0031-9155/61/13/4796

Cited by 38 publications

(31 citation statements)

References 82 publications

(233 reference statements)

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“…The machine learning algorithms employed in this study are the Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), Naïve Bayes (NB), Bayesian Network (BNet), k-Nearest Neighbours (k-NN), C4.5 (J48), Alternate Decision Tree (ADTree), Logistic Model Trees (LMT), AdaBoostM1 (AdaBoost) and Support Vector Machine (SVM). Following our previous studies, 19,20 we used the CVParame-terSelection and GridSearch techniques to select associated hyper-parameters for each classifier (available in the WEKA data mining suite).…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Classification of mammographic microcalcification clusters with machine learning confidence levels

Rampun¹,

Wang²,

Scotney³

et al. 2018

14th International Workshop on Breast Imaging (IWBI 2018)

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This paper presents a novel investigation of machine learning performance by examining probability outputs in conjunction with classification accuracy (CA) and area under the curve (AU C). One of the main issues in the deployment of computer-aided detection/diagnosis (CAD) systems is lack of 'trust' of clinicians in the CAD system, increasing the possibility of the system not being used. Whilst most authors evaluate the performance of their breast CAD systems based on CA and AU C, we study the distribution of the classifiers' probability outputs and use it as an additional confidence level metric to indicate the reliability of a computer system. Experimental results suggest that although most classifiers produce similar results in terms of CA and AU C (less than 2% variation), their performances are significantly different when considering confidence level (10 to 25% difference). This study may provide opportunities for refining radiologists' interaction with CAD systems and improving the reliability of CAD systems as well as diagnostic decision making in medicine with high CA or AU C with high degree of certainty.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Classification of mammographic microcalcification clusters with machine learning confidence levels

Rampun¹,

Wang²,

Scotney³

et al. 2018

14th International Workshop on Breast Imaging (IWBI 2018)

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“…This suggests that using a combination of medium sizes of R captures more discriminant features regardless of the topology. Previous studies [37][38][39] on texture descriptors across different window sizes found that using a small value of R does not capture sufficient information about the regions due to the limited intensities and grey level variations. On the other hand, using a large R tends to alter the actual representation of the area, especially when one class dominates over another class.…”

Section: Results On Different Multiresolutionsmentioning

confidence: 99%

Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies

et al. 2018

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This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro-glandular disk region (FGD roi ) instead of the whole breast area as the majority of current studies have done. We take a multiresolution and multi-orientation approach, investigate the effects of various neighbourhood topologies and select dominant patterns to maximise texture information. Subsequently, the Support Vector Machine classifier is used to perform the classification, and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method produced competitive results up to 86.13% and 82.02% accuracy based on 322 and 206 mammograms taken from the Mammographic Image Analysis Society (MIAS) and InBreast datasets, which is comparable with the state-of-the-art in the literature.

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“…Combining prediction results of several classifiers is a common approach in machine learning [26], [27]. The main advantage of this approach is that it prevents a single model from being exploited by outliers/noise/complicated cases.…”

Section: Discussionmentioning

confidence: 99%

Breast Mass Classification in Mammograms using Ensemble Convolutional Neural Networks

Rampun

Scotney

Morrow

et al. 2018

2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom)

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The paper presents quantitative results of a preliminary study undertaken as part of Decision Support and Information Management System for Breast Cancer (DESIREE). DESIREE is a European-funded project to improve the management of primary breast cancer through image-based, guidelinebased, experience-based, and case-based information systems. In this study we explore the use of ensemble deep learning for breast mass classification in mammograms. The proposed method is based on AlexNet with some modifications in order to adapt it to our classification problem. Subsequently, model selection is performed to select the best three results based on the highest validation accuracies during the validation phase. Finally, the prediction is based on the average probability of the models. Experimental evaluation shows that accuracy from individual models ranges between 75% and 77%, but combining the best models (ensemble networks) results in over 80% classification accuracy and aura under the curve.

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Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone

Cited by 38 publications

References 82 publications

Classification of mammographic microcalcification clusters with machine learning confidence levels

Classification of mammographic microcalcification clusters with machine learning confidence levels

Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies

Breast Mass Classification in Mammograms using Ensemble Convolutional Neural Networks

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