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An Intelligent Recurrent Neural Network with Long Short-Term Memory (LSTM) BASED Batch Normalization for Medical

R Rajeev1, J Abdul Samath2, N K Karthikeyan3

  • 1Department of Information Technology, Sri Ramakrishna College of Arts and Science (Formerly SNR Sons College), Coimbatore, Tamil Nadu, India. rajeevathira@gmail.com.

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This study introduces a novel medical image denoising method using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) with Batch Normalization. The technique effectively removes noise from lung CT images, outperforming existing methods.

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Batch normalizationImage denoisingLong short-term memoryRecurrent neural network

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Artificial Intelligence

Background:

  • Image noise is a persistent challenge in medical imaging, affecting diagnostic accuracy.
  • Traditional denoising methods often struggle with complex noise patterns like white, salt-and-pepper noise.

Purpose of the Study:

  • To develop an advanced denoising system for medical images, specifically lung CT scans.
  • To improve the signal-to-noise ratio (SNR) and reduce Mean Square Error (MSE) in noisy images.

Main Methods:

  • A hybrid approach combining Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) based Batch Normalization.
  • Utilizing Particle Swarm Optimization (PSO) to determine an optimal batch size for the network.
  • Applying the developed algorithm to denoise lung CT images corrupted by various noise types.

Main Results:

  • The proposed method demonstrated significant noise reduction capabilities.
  • Quantitative assessment showed competitive performance in terms of Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) compared to existing techniques.
  • Experimental outcomes validated the algorithm's effectiveness.

Conclusions:

  • The integrated RNN-LSTM with Batch Normalization offers a robust solution for medical image denoising.
  • This approach presents a promising advancement in signal and image processing for enhanced medical image quality.
  • The method is effective in removing common noise types from lung CT images.