Table of content

1. Problem solving

• Using CNNs to diagnose pneumonia from chest X-ray images.
• Explanation of the project's aim to enhance diagnostic accuracy using advanced technology.

2. Dataset Overview

Description of the "Chest X-Ray Images (Pneumonia)" dataset sourced from Kaggle.
Organization of data into train, test, and validation sets with categories for Pneumonia and Normal images.

3. Models Employed

Conventional CNN model and pretrained VGG16 model. Deep learning framework used: TensorFlow.

4. Model Structures and Functionalities

Conventional CNN Model:

• Architecture from input to output layers.
• Explanation of each layer's function including convolutional, max pooling, dense, and dropout layers.

VGG16 Model:

*Composition of the VGG16 network.

• Adjustments made for pneumonia classification and the rationale behind using pretrained models.

5. Model Evaluation and Implementation

Data Preprocessing:

• Techniques for loading, resizing, and augmenting the data.
• Importance of normalization.

Model Training and Compilation:

• Configuration of models including optimizer and loss function selection.
• Training duration and conditions.

Model Validation and Testing:

• Split details and methodology for training/validation/testing.
• Use of ROC curve and AUC for performance evaluation.
  

6. Results and Discussion

• Presentation of training outcomes, accuracy, and loss metrics.
• Discussion on overfitting observed during validation and the steps taken to address it.

7. Conclusion

• Summary of the study's findings.
• Assessment of the models' effectiveness in improving pneumonia diagnostics.
• Future directions for research to enhance model generalizability.

8. References

• Comprehensive listing of all sources cited in the report.
  

For full report, you maybe download within this repository.
Files name= 21_pages_report.pdf