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Related Experiment Video

Updated: Jun 29, 2025

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Concatenated Modified LeNet Approach for Classifying Pneumonia Images.

Dhayanithi Jaganathan1, Sathiyabhama Balsubramaniam1, Vidhushavarshini Sureshkumar2

  • 1Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India.

Journal of Personalized Medicine
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

A novel deep learning model, the Concatenated Modified LeNet classifier, accurately detects pneumonia from medical images with 96% accuracy. This advancement offers efficient pneumonia diagnosis for improved patient care and timely treatment.

Keywords:
ReLUclassificationconvolution neural networkmodified LeNetpneumonia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Pneumonia is a significant global health issue requiring advanced diagnostic methods.
  • Current diagnostic tools for pneumonia can be improved with more efficient and accurate techniques.
  • Deep learning offers potential for enhancing medical image analysis and disease detection.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate pneumonia image classification.
  • To improve the discriminative capacity and performance of Convolutional Neural Network (CNN) architectures for pneumonia diagnosis.
  • To assess the efficacy of a modified LeNet architecture incorporating ReLU and batch normalization.

Main Methods:

  • Implementation of a concatenated modified LeNet classifier utilizing deep learning.
  • Incorporation of a revised Rectified Linear Unit (ReLU) activation function to enhance feature learning.
  • Integration of batch normalization to stabilize training and improve performance in CNNs.

Main Results:

  • The Concatenated Modified LeNet classifier achieved a 96% accuracy rate in pneumonia image recognition.
  • The model demonstrated high recognition rates when benchmarked against other deep learning models.
  • Modifications, including ReLU and batch normalization, helped prevent overfitting and reduced computational time.

Conclusions:

  • The Concatenated Modified LeNet classifier shows significant potential as a tool for medical professionals in diagnosing pneumonia.
  • Accurate and efficient image classification by the model can lead to better treatment decisions and patient outcomes.
  • This deep learning approach contributes to advancing diagnostic capabilities for pneumonia.