Deep Learning-Based Disk Herniation Computer Aided Diagnosis System From MRI Axial Scans

Alsmirat, Mohammad A.; Al-Mnayyis, Nusaiba; Al-Ayyoub, Mahmoud; Al-Mnayyis, Asma’a

doi:10.1109/access.2022.3158682

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Studies have demonstrated that data augmentation, when combined with fine-tuning and transfer learning, can significantly enhance model accuracy [119,120]. Additionally, data augmentation can be used to enhance the robustness of CNN models to noise for improved training [121,122] and to mitigate kernel saturation (to increase classification accuracy) [123]. Therefore, data augmentation techniques can be used to develop robust CNN diagnostic models by addressing limited training data, noise and kernel saturation.…”

Section: Data Augmentation For Training a Robust Cnn Diagnostic Model...mentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

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Section: Data Augmentation For Training a Robust Cnn Diagnostic Model...mentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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“…Building the model and experimental setupsSince 2012, Deep Learning (DL) has witnessed great success in computer vision and other disciplines such as speech recognition, natural language processing and modeling[19] . The success of DL is based on the recent availability of big data, high computational power, and the utilization of the powerful Artificial Neural Network (ANN) algorithms.…”

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confidence: 99%

Video coding deep learning-based modeling for long life video streaming over next network generation

et al. 2023

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Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment.

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Policy Learning for Actively Labeled Sample Selection on Lumbar Semi-supervised Classification

Hai,

Chen,

Qiao

et al. 2024

J Digit Imaging. Inform. med.

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Deep Learning-Based Disk Herniation Computer Aided Diagnosis System From MRI Axial Scans

Cited by 7 publications

References 34 publications

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Video coding deep learning-based modeling for long life video streaming over next network generation

Policy Learning for Actively Labeled Sample Selection on Lumbar Semi-supervised Classification

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