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The synergy between CNN and LSTM networks has been found to be very effective in processing complex data sets. While CNNs are particularly good at detecting spatial features, LSTMs are particularly good at detecting temporal dependencies. By combining both, their performance can be enhanced in tasks involving both spatial and sequential data analysis. Image recognition, especially of sequential image patterns or video frames, highly leverages this synergy. This paper reports a full review of CNNs, LSTMs, and their hybrid CNN-LSTM networks In the field of image recognition. A comparative analysis compares their structural variations, domains of application, and performance. With extensively utilized datasets like CIFAR-10 and ImageNet, the Models undergo testing to evaluate their spatial and temporal data processing ability. Metrics like accuracy, inference time, and robustness are represented by column and line charts, depicting the hybrid model’s superiority over independent architectures. Furthermore, the paper introduces an innovative block diagram and algorithmic framework specific to CNN-LSTM models for image recognition tasks. The findings show that hy-brid architectures yield higher accuracy and reliability, especially in situations requiring concomitant feature extraction and sequential pattern learning. The results present insides and suggestions for further research into image recognition and allied fields.

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Evaluation of CNN, LSTM, and Hybrid CNN-LSTM Models for Sequential Image Recognition Tasks: Insights into Accuracy and Computational Performance

  • 会议论文

  • 发表日期：

    2026年

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  • Anvitha R. Mayya,
  • Aaryan Mahendra Kaurwar,
  • Harsh Rutuparn Modak,
  • Rupesh C. Jaiswal,
  • Anagha V. Rajput,
  • Lalit P. Patil

摘要

The synergy between CNN and LSTM networks has been found to be very effective in processing complex data sets. While CNNs are particularly good at detecting spatial features, LSTMs are particularly good at detecting temporal dependencies. By combining both, their performance can be enhanced in tasks involving both spatial and sequential data analysis. Image recognition, especially of sequential image patterns or video frames, highly leverages this synergy. This paper reports a full review of CNNs, LSTMs, and their hybrid CNN-LSTM networks In the field of image recognition. A comparative analysis compares their structural variations, domains of application, and performance. With extensively utilized datasets like CIFAR-10 and ImageNet, the Models undergo testing to evaluate their spatial and temporal data processing ability. Metrics like accuracy, inference time, and robustness are represented by column and line charts, depicting the hybrid model’s superiority over independent architectures. Furthermore, the paper introduces an innovative block diagram and algorithmic framework specific to CNN-LSTM models for image recognition tasks. The findings show that hy-brid architectures yield higher accuracy and reliability, especially in situations requiring concomitant feature extraction and sequential pattern learning. The results present insides and suggestions for further research into image recognition and allied fields.