What question did this study set out to answer?

The aim is to improve image recognition accuracy using a new CNN architecture while minimizing resource consumption.

January 18, 2026Open Access

Image Recognition Using Enhanced Convolutional Neural Networks

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The aim is to improve image recognition accuracy using a new CNN architecture while minimizing resource consumption.
Developed an improved CNN architecture with smaller convolutional kernels
Organized deeper stacks of layers in the model
Regularized the model during training
Evaluated using the ImageNet Large Scale Visual Recognition Challenge dataset
Achieved a Top-5 error rate of 9.18%
Outperformed multiple state-of-the-art methods
Demonstrated high scalability properties
Maintained low training costs

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Implication

Experimental analysis demonstrates improved accuracy in image recognition using a new CNN architecture, suggesting effective advancements in visual processing technology.

Cite This Study

Kumar et al. (2025) studied this question.

synapsesocial.com/papers/696c7791eb60fb80d1395c63 https://doi.org/10.5281/zenodo.18113670

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Implication

Experimental analysis demonstrates improved accuracy in image recognition using a new CNN architecture, suggesting effective advancements in visual processing technology.

Image Recognition Using Enhanced Convolutional Neural Networks

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Discussion

Implication

Cite This Study

Image Recognition Using Enhanced Convolutional Neural Networks

Key Points

Discussion

Implication

Cite This Study

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