Concatenated Modified LeNet Approach for Classifying Pneumonia Images

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.