Deep Convolutional Neural Networks for Unconstrained Ear Recognition

Alshazly, Hammam; Linse, Christoph; Barth, Erhardt; Martinetz, Thomas

doi:10.1109/access.2020.3024116

Cited by 51 publications

(37 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, we addressed the problem by placing the images into a fixed-sized canvas determined specifically for each CNN architecture, where the aspect ratio of the original image was preserved. This proved to be a less violating and more effective procedure to achieve better results, as reported in [ 12 ]. Moreover, we utilized the layer-wise adaptive large batch optimization technique called LAMB [ 13 ], which has demonstrated better performance and convergence speed for training deep networks.…”

Section: Introductionmentioning

confidence: 73%

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Alshazly

Linse

Barth

et al. 2021

Sensors

Self Cite

165

115

View full text Add to dashboard Cite

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.

show abstract

Section: Introductionmentioning

confidence: 73%

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Alshazly

Linse

Barth

et al. 2021

Sensors

Self Cite

165

115

View full text Add to dashboard Cite

show abstract

“… [ 75 ] UERC ResNet KNN 74.6 - 6. [ 6 ] EarVn1.0 ResNetXt101 Softmax 93.45 - 7. [ 76 ] IITD-II and AMI CNN Softmax IITD-II 97.36 and AMI 96.99 - 8.…”

Section: Taxonomic Review On Ear Biometricmentioning

confidence: 99%

“…They major disadvantage is that they have employed existing methods for ear detection, and recognition and the work have limited novelty. In a recent study [ 6 ], the authors explored the use of deep learning models such as VGG, ResNetx, and Inception. They have employed various learning strategies such as feature extraction, fine-tuning, and ensemble learning.…”

Section: Taxonomic Review On Ear Biometricmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive survey and deep learning-based approach for human recognition using ear biometric

2021

View full text Add to dashboard Cite

Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development.

show abstract

“…We opted for a different preprocessing procedure and experimentally investigated an approach to preserve the aspect ratios of the CT images. This procedure has proved to be very effective and results in an improved overall performance ( Alshazly et al, 2020 ). Extensive experiments and analysis on the diagnostic accuracy using standard evaluation metrics were conducted against five standard CNN models.…”

Section: Introductionmentioning

confidence: 99%

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans

Alshazly

Linse

Abdalla

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.

show abstract

Deep Convolutional Neural Networks for Unconstrained Ear Recognition

Cited by 51 publications

References 59 publications

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

A comprehensive survey and deep learning-based approach for human recognition using ear biometric

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans

Contact Info

Product

Resources

About