Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network

Novac, Ovidiu-Constantin; Oproescu, Mihai; Novac, Cornelia Mihaela; Bizon, Nicu; Oprescu, Mihai; Stan, Ovidiu; Gordan, Cornelia Emilia

doi:10.3390/s22228872

Cited by 14 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…376,377 In their comparison of these two libraries, Novac et al concluded that PyTorch offers a more beginnerfriendly experience with faster training and execution, while TensorFlow allows for higher flexibility and better resulting accuracy. 378 These top imported libraries represent clear starting points for those who are new to the field and want to start understanding, writing, and using ML algorithms.…”

Section: Discussionmentioning

confidence: 99%

Open-Source Machine Learning in Computational Chemistry

Hagg

2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

show abstract

Section: Discussionmentioning

confidence: 99%

Open-Source Machine Learning in Computational Chemistry

Hagg

2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…According to the mean of the Dice coefficient in the validation set, the best model was chosen for automatic segmentation. Our network was developed using the python package “Pytorch” and trained using RTX-2080Ti in the cloud computation platform “AI-Galaxy” ( http://www.ai-galaxy.cn/ ) [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features

Yang,

Wang,

Zhang

et al. 2024

BMC Med Imaging

View full text Add to dashboard Cite

Background This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Another purpose of our work was to develop a radiomics model based on the radiomics features extracted from automatic segmentation to differentiate low- and high-grade meningiomas before surgery. Materials A total of 326 patients with pathologically confirmed meningiomas were enrolled. Samples were randomly split with a 6:2:2 ratio to the training set, validation set, and test set. Volumetric regions of interest (VOIs) were manually drawn on each slice using the ITK-SNAP software. An automatic segmentation model based on SegResNet was developed for the meningioma segmentation. Segmentation performance was evaluated by dice coefficient and 95% Hausdorff distance. Intra class correlation (ICC) analysis was applied to assess the agreement between radiomic features from manual and automatic segmentations. Radiomics features derived from automatic segmentation were extracted by pyradiomics. After feature selection, a model for meningiomas grading was built. Results The DLM detected meningiomas in all cases. For automatic segmentation, the mean dice coefficient and 95% Hausdorff distance were 0.881 (95% CI: 0.851–0.981) and 2.016 (95% CI:1.439–3.158) in the test set, respectively. Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636–0.933). Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636–0.933). For meningioma classification, the radiomics model based on automatic segmentation performed well in grading meningiomas, yielding a sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.778 (95% CI: 0.701–0.856), 0.860 (95% CI: 0.722–0.908), 0.848 (95% CI: 0.715–0.903) and 0.842 (95% CI: 0.807–0.895) in the test set, respectively. Conclusions The DLM yielded favorable automated detection and segmentation of meningioma and can help deploy radiomics for preoperative meningioma differentiation in clinical practice.

show abstract

“…Pretrained models such as Xception, VGG16, DenseNet121, and ResNet50 were used. The models were imported from TensorFlow [18] and trained using the dataset. Figure 4 The ReLU function has an advantage over other activation functions in that it does not stimulate all neurons simultaneously and can be used to enhance non-linearity.…”

Section: Convolutional Neural Network Modelmentioning

confidence: 99%

“…Pretrained models such as Xception, VGG16, DenseNet121, and ResNet50 were used. The models were imported from TensorFlow [18] and trained using the dataset. Figure 4 shows the model development methodology.…”

Section: Convolutional Neural Network Modelmentioning

confidence: 99%

Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images

Aljameel

Althumairy

Albassam

et al. 2023

BDCC

View full text Add to dashboard Cite

Predicting dental development in individuals, especially children, is important in evaluating dental maturity and determining the factors that influence the development of teeth and growth of jaws. Dental development can be accelerated in patients with an accelerated skeletal growth rate and can be related to the skeletal growth pattern as a child. The dental age (DA) of an individual is essential to the dentist for planning treatment in relation to maxillofacial growth. A deep-learning-based regression model was developed in this study using panoramic radiograph images to predict DA. The dataset included 529 samples of panoramic radiographs collected from the dental hospital at Imam Abdulrahman Bin Faisal university in Saudi Arabia. Different deep learning methods were applied to implement the model, including Xception, VGG16, DenseNet121, and ResNet50. The results indicated that the Xception model had the best performance, with an error rate of 1.417 for the 6–11 age group. The proposed model can assist the dentist in determining the appropriate treatment for patients based on their DA rather than their chronological age.

show abstract

Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network

Cited by 14 publications

References 21 publications

Open-Source Machine Learning in Computational Chemistry

Open-Source Machine Learning in Computational Chemistry

Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features

Predictive Artificial Intelligence Model for Detecting Dental Age Using Panoramic Radiograph Images

Contact Info

Product

Resources

About