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Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based

Tammineedi Venkata Satya Vivek1, Ayesha Naureen2, Mohd Shaikhul Ashraf3

  • 1Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.

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This study introduces a novel deep learning approach for analyzing microscopic images to aid in disease diagnosis. The method enhances accuracy in identifying diseases like cancer from medical images.

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Area of Science:

  • Biomedical imaging
  • Computational pathology
  • Deep learning in medicine

Background:

  • Microscopy image analysis is vital for disease characterization and computer-assisted diagnosis.
  • Precise analysis of biological image sequences is a significant challenge.
  • Deep learning methods are increasingly adopted for bioimage processing.

Purpose of the Study:

  • To propose novel deep learning techniques for biomedical microscopic image analysis.
  • To enhance feature extraction and classification for improved diagnostic accuracy.
  • To develop advanced methods for computer-assisted diagnosis and prognosis.

Main Methods:

  • Microscopic images were preprocessed including noise removal, edge smoothing, and normalization.
  • Feature extraction was performed using a ConVol_NN architecture with the AlexNet model.
  • Feature classification utilized an ensemble of Inception-ResNet and VGG-16 (EN_InResNet_VGG-16) architectures.

Main Results:

  • The proposed method achieved high accuracy (98%) and precision (90%) in microscopic image analysis.
  • Evaluations demonstrated a signal-to-noise ratio (SNR) of 89% and mean squared error (MSE) of 62%.
  • The computational time for analysis was reported as 79%.

Conclusions:

  • The developed deep learning approach offers a powerful tool for quantitative analysis of microscopic images.
  • This technique can significantly improve the accuracy and efficiency of computer-assisted diagnosis for various diseases.
  • The study highlights the potential of integrated deep learning architectures in advancing biomedical image analysis.