Multi-scale directional-filtering-based method for follicular lymphoma grading

Bozkurt, Alican; Suhre, Alexander; Çetin, A. Enis

doi:10.1007/s11760-014-0681-0

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…The details of the filters and how they were obtained are shown in Refs. [19,20]. We postulated that the edgehighlighted images produced by the directed filters will aid in classification.…”

Section: Overview Of the Data Setmentioning

confidence: 99%

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages

Atici

Ansari

Allareddy

et al. 2023

Orthod Craniofacial Res

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ObjectiveA study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre‐processing layer that takes X‐ray images and the age as the input is proposed.MethodsA total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model‐fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom‐designed CNN model with the directional filters.ResultsThe proposed innovative model that uses a parallel structured network preceded with a pre‐processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects.ConclusionAggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.

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Section: Overview Of the Data Setmentioning

confidence: 99%

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages

Atici

Ansari

Allareddy

et al. 2023

Orthod Craniofacial Res

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“…To emphasize the edge information in the image, directional őlters are used as pre-processing layers to the base networks. The details of the directional őlters can be found in [38,39]. The tripod network structure allows for an output feature vector of size 192, which is the combination of the three base networks.…”

Section: Deep Neural Network Structurementioning

confidence: 99%

A Novel Continuous Classification System for the Cervical Vertebrae Maturation (CVM) Stages Using Convolutional Neural Networks

Atici

Elnagar

Allareddy

et al. 2023

Preprint

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Introduction: We aim to apply deep learning methods to achieve the continuous classification of the Cervical Vertebrae Maturation (CVM) stages and to assess skeletal maturity. We propose a novel two-stage system with a parallel structure network and a sigmoid-based method to generate the continuous-valued cervical vertebrae maturity (CVCVM) parameter. Methods: A total of 1398 Cephalometric radiographs are meticulously annotated and stratified based on their respective Cervical Vertebrae Maturation (CVM) stages, with 1018 images allocated for training and validation, also the remaining 380 collected from 25 patients and labeled by two clinicians for testing. The images are further partitioned according to gender. A two-stage system is devised for the continuous estimation of CVM stages. A parallel-structure neural network called TriPodNet is trained to gauge the likelihood of each class for the maturation stage in the first part of the proposed system. The network is supplied with two different types of input, namely a radiographic X-ray image and chronological age. Probability values of individual classes are generated and mapped onto a continuous stage by two different methods, namely weighted averaging and sigmoid-based regression. The correlation of the estimated Cervical Vertebrae Maturation parameter is assessed using the Pearson correlation coefficient. In order to ascertain the validity of TriPodNet, the Permutation Importancemethod is employed to gauge the impact of each input. Results: TriPodNet is able to achieve a validation accuracy of 81.17% for female subjects and 75.96% for male subjects. During testing, the class probability values of the inputs are determined by TriPodNet, and the continuous estimation parameters are obtained by applying two distinct mapping functions. The sigmoid-based regression method produces an average correlation coefficient value of 0.910 with the first clinician and 0.944 with the second clinician for male patients, while for female patients the values were 0.910 and 0.918 respectively. Conversely, the weighted average method performs less effectively, with average correlation coefficient values of 0.913 and 0.904 for male patients with the first and second clinicians respectively. For female patients, the method produces similar results with an average correlation coefficient value of 0.901 and 0.896 with the first and second clinicians respectively. The Permutation Importance method shows that the image input and the chronological age input collaboratively contribute to the model in producing the accurate output. Conclusion: The proposed method to determine the CVM stages in a continuous stages pattern CVCVM using Convolutional Neural Network (CNN) achieved novel high correlation results compared to true labels and it is more consistent with the gradual growth changes. It is observed to be a unique way to represent skeletal maturity and assess growth.

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“…Therefore, the resizing should be done without changing the aspect ratio of the image. Next, instead of feeding the resized ROI images directly to the DL networks, the edges of vertebral bodies in the ROI images are emphasized using eight directional filters described in [26,27]. The outputs of directional filters are fed to the DL networks.…”

Section: A Image Roi Segmentationmentioning

confidence: 99%

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters

et al. 2022

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Introduction We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images. Methods A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were cropped to extract the cervical vertebrae using an Aggregate Channel Features (ACF) object detector. The resulting images were used to train four different Deep Learning (DL) models: our proposed CNN, MobileNetV2, ResNet101, and Xception, together with a set of tunable directional edge enhancers. When using MobileNetV2, ResNet101 and Xception, data augmentation is adopted to allow adequate network complexity while avoiding overfitting. The performance of our CNN model was compared with that of MobileNetV2, ResNet101 and Xception with and without the use of directional filters. For validation and performance assessment, k-fold cross-validation, ROC curves, and p-values were used. Results The proposed innovative model that uses a CNN preceded with a layer of tunable directional filters achieved a validation accuracy of 84.63%84.63% in CVM stage classification into five classes, exceeding the accuracy achieved with the other DL models investigated. MobileNetV2, ResNet101 and Xception used with directional filters attained accuracies of 78.54%, 74.10%, and 80.86%, respectively. The custom-designed CNN method also achieves 75.11% in six-class CVM stage classification. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If the custom-designed CNN is used without the directional filters, the test accuracy decreases to 80.75%. In the Xception model without the directional filters, the testing accuracy drops slightly to 79.42% in the five-class CVM stage classification. Conclusion The proposed model of a custom-designed CNN together with the tunable Directional Filters (CNNDF) is observed to provide higher accuracy than the commonly used pre-trained network models that we investigated in the fully automated determination of the CVM stages.

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Multi-scale directional-filtering-based method for follicular lymphoma grading

Cited by 10 publications

References 23 publications

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages

A Novel Continuous Classification System for the Cervical Vertebrae Maturation (CVM) Stages Using Convolutional Neural Networks

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters

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