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This is a Quantitative Study study (also classified as: Experimental Study).

September 23, 2025International Journal of Engineering Technology and Management SciencesOpen Access

Deep Convolutional Neural Network Architecture for Enhanced Image Classification

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The proposed CNN model achieves an accuracy of 94.2%, significantly higher than baseline models.
Experiments reveal that utilizing batch normalization and dropout regularization enhances generalization performance.
Ablation studies confirm the effectiveness of the proposed methods in improving image classification tasks.
The publicly available model and training code enable reproducibility and community engagement.

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Implication

Proposed CNN architecture improves generalization performance and accuracy in image classification, highlighting key techniques like dropout and batch normalization.

Cite This Study

Kumar et al. (2023) studied this question.

synapsesocial.com/papers/68d4768331b076d99fa6ef5b https://doi.org/10.46647/ijetms.2023.v07i05.068

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Implication

Proposed CNN architecture improves generalization performance and accuracy in image classification, highlighting key techniques like dropout and batch normalization.

Deep Convolutional Neural Network Architecture for Enhanced Image Classification

Key Points

Discussion

Implication

Cite This Study

Deep Convolutional Neural Network Architecture for Enhanced Image Classification

Key Points

Discussion

Implication

Cite This Study

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