Synaptic metaplasticity for image processing enhancement in convolutional neural networks

Vives-Boix, Víctor; Ruiz-Fernández, Daniel

doi:10.1016/j.neucom.2021.08.021

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…The DenseNet draw inspiration from this idea by adding quick connections in the network model, where gradient values are transmitted through quick connections in the network. At the same time, the DenseNet also uses feature reuse to reduce the amount of model parameters 27 – 29 .…”

Section: Design Of An Accurate Ir Model Combining Densenet and Gqmentioning

confidence: 99%

The application of improved densenet algorithm in accurate image recognition

Hou,

Wu,

Cai

et al. 2024

Sci Rep

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Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume. The introduction of quantization error reduces the impact of gradient loss on model convergence. The test results show that the improvement strategy designed by the research improves the model parameter efficiency while ensuring the recognition effect. Narrowing the learning rate is conducive to refining the updating granularity of model parameters, and deepening the number of network layers can effectively improve the final recognition accuracy and convergence effect of the model. It is better than the existing state-of-the-art image recognition models, visual geometry group and EfficientNet. The parallel acceleration algorithm, which is improved by the gradient quantization, performs better than the traditional synchronous data parallel algorithm, and the improvement of the acceleration ratio is obvious. Compared with the traditional synchronous data parallel algorithm and stale synchronous parallel algorithm, the optimized parallel acceleration algorithm of the study ensures the image data training speed and solves the bottleneck problem of communication data. The model designed by the research improves the accuracy and training speed of image recognition technology and expands the use of image recognition technology in the field of computer vision.Please confirm the affiliation details of [1] is correct.The relevant detailed information in reference [1] has been confirmed to be correct.

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Section: Design Of An Accurate Ir Model Combining Densenet and Gqmentioning

confidence: 99%

The application of improved densenet algorithm in accurate image recognition

Hou,

Wu,

Cai

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…There are several well-known CNN models, including LeNet-5, Google Net, AlexNet, and Net in System [14,15].…”

Section: Fully Connected Layermentioning

confidence: 99%

Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest

Ramesh¹,

Hrishikesh²,

Patil

2023

IJRITCC

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Infant MRI brain soft tissue segmentation become more difficult task compare with adult MRI brain tissue segmentation, due to Infant’s brain have a very low Signal to noise ratio among the white matter_WM and the gray matter _GM. Due the fast improvement of the overall brain at this time , the overall shape and appearance of the brain differs significantly. Manual segmentation of anomalous tissues is time-consuming and unpleasant. Essential Feature extraction in traditional machine algorithm is based on experts, required prior knowledge and also system sensitivity has change. Recently, bio-medical image segmentation based on deep learning has presented significant potential in becoming an important element of the clinical assessment process. Inspired by the mentioned objective, we introduce a methodology for analysing infant image in order to appropriately segment tissue of infant MRI images. In this paper, we integrated random forest classifier along with deep convolutional neural networks (CNN) for segmentation of infants MRI of Iseg 2017 dataset. We segmented infants MRI brain images into such as WM- white matter, GM-gray matter and CSF-cerebrospinal fluid tissues, the obtained result show that the recommended integrated CNN-RF method outperforms and archives a superior DSC-Dice similarity coefficient, MHD-Modified Hausdorff distance and ASD-Average surface distance for respective segmented tissue of infants brain MRI.

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