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The Model recognizes the X-Ray Image and categorize/predict into a Normal Condition X-Ray Image or a Potential Pneumonia Condition X-Ray Image to reduce the spreading of infamous Pneumonia disease, especially in the times of COVID-19. All chest X-ray imaging during data gathering was performed as part of patients’ routine clinical care. For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans by Guangzhou Women and Children’s Medical Center, Guangzhou.

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x-ray-image-processing-pneumonia-prediction-model's Introduction

X-Ray Image Prediction Pneumonia

Introduction

This project aims to develop a deep learning model to predict pneumonia from chest X-ray images. Pneumonia is an inflammatory condition of the lung affecting primarily the small air sacs known as alveoli. It can be caused by various factors including infections with viruses or bacteria.

Dataset Description

The dataset comprises X-ray images obtained from pediatric patients aged one to five years old from Guangzhou Women and Children’s Medical Center. The images are organized into three folders: train, test, and validation. Each folder contains subfolders for Pneumonia and Normal cases.

Model Architecture

The deep learning model is built using the Keras Sequential API with the following architecture:

  • Convolutional layers (Conv2D)
  • Batch normalization layers (BatchNormalization)
  • Max-pooling layers (MaxPool2D)
  • Dropout layers (Dropout)
  • Flatten layer
  • Dense layers (Dense)

CONVOLUTIONAL NEURAL NETWORK IMAGE PROCESSING TECHNIQUE OF MAX POOLING VISUALIZATION

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Data Preprocessing

  • Images are resized to a standard dimension (150x150).
  • Grayscale normalization is applied.
  • Data augmentation techniques such as rotation, zooming, and flipping are used to increase dataset diversity.

Model Training

  • The model is trained using mini-batch gradient descent with the RMSprop optimizer.
  • Learning rate reduction is implemented to dynamically adjust the learning rate during training.
  • The model is trained for 12 epochs.

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Evaluation and Analysis

  • Model performance is evaluated on the test dataset using metrics like accuracy, loss, precision, recall, and F1-score.
  • Confusion matrix is generated to visualize model predictions against true labels.
  • Training and validation accuracy/loss curves are plotted for analysis.

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Conclusion

The developed model demonstrates reasonable performance in pneumonia detection from chest X-ray images. Further improvements can be made by fine-tuning hyperparameters and exploring advanced techniques.

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