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<p>Advanced deep learning (DL) techniques are essential for accurate medical image analysis. The remarkable achievements of DL indicate that it has the potential to transform medical image analysis. DL-based medical image analysis can offer real-time inputs significantly improving healthcare outcomes and efficiency. This comprehensive review explores the latest DL techniques designed explicitly for healthcare medical image analysis. While some segmentation results are satisfactory, most models have complex architectures that require substantial time for training and testing. The preprocessing strategy streamlines the segmentation process by focusing on a small section of the image. This approach accelerates computation by reducing processing time and mitigating over-fitting in the Deep Learning model when it overly specializes in the training data. The utilization of a Recursive Convolutional Refinement Network (RCRN) for practical brain image analysis. This model employs an attention mechanism to combine local details and global context from brain images. The present is an innovative attention mechanism technique for enhancing accuracy in brain tumor delineation. The attention mechanism clarifies the tumor’s center and spatial position within the brain. Following rigorous testing, the model performs competitively on the BraTS 2018 brain tumor MRI dataset with performance of 99% accuracy, 96% precision, 96.2% recall, 96.8% F-measure and 97.1% IMoU and 0.15 p-value for tumor core prediction.</p>

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Refining Brain Image Interpretation Using Recursive Convolutional Networks: A Robust RCRN-Based Framework

  • 期刊论文

  • 发表日期：

    2025年12月11日

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  • S. Shaikshavali,
  • Vamsidhar Enireddy

摘要

Advanced deep learning (DL) techniques are essential for accurate medical image analysis. The remarkable achievements of DL indicate that it has the potential to transform medical image analysis. DL-based medical image analysis can offer real-time inputs significantly improving healthcare outcomes and efficiency. This comprehensive review explores the latest DL techniques designed explicitly for healthcare medical image analysis. While some segmentation results are satisfactory, most models have complex architectures that require substantial time for training and testing. The preprocessing strategy streamlines the segmentation process by focusing on a small section of the image. This approach accelerates computation by reducing processing time and mitigating over-fitting in the Deep Learning model when it overly specializes in the training data. The utilization of a Recursive Convolutional Refinement Network (RCRN) for practical brain image analysis. This model employs an attention mechanism to combine local details and global context from brain images. The present is an innovative attention mechanism technique for enhancing accuracy in brain tumor delineation. The attention mechanism clarifies the tumor’s center and spatial position within the brain. Following rigorous testing, the model performs competitively on the BraTS 2018 brain tumor MRI dataset with performance of 99% accuracy, 96% precision, 96.2% recall, 96.8% F-measure and 97.1% IMoU and 0.15 p-value for tumor core prediction.