M.S. AAI Capstone Chronicles 2024

CNN Lung Disease Classification

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superior performance in image classification tasks. We also plan to implement attention mechanisms to help the model focus on the most relevant parts of the chest X-rays, potentially improving both accuracy and interpretability (Guan et al., 2018). Given the multi-label nature of our dataset, investigating multi-task learning approaches for simultaneous detection of multiple conditions could significantly enhance overall performance (Ruder, 2017). In terms of data enhancement, we intend to acquire a more diverse dataset with bounding boxes to improve the model's generalization capabilities. We will also enhance our data augmentation techniques, incorporating methods like color jittering and noise injection to increase model robustness (Shorten & Khoshgoftaar, 2019). Additionally, we plan to incorporate clinical metadata to supplement image data, providing additional context that could enhance classification accuracy (Baltruschat et al., 2019). Regarding deployment, we will develop a user-friendly interface for radiologists to easily input X-ray images and receive model predictions. This will be supported by robust data pipelines for real-time processing of X-ray images in a clinical setting. To ensure compliance with healthcare data regulations (e.g., HIPAA), we will implement appropriate data security and privacy measures. Extensive clinical validation studies will be conducted to assess the model's performance in real-world scenarios. Post-deployment would involve establishing a monitoring system for continuous evaluation of model performance and implementing a feedback mechanism for radiologists to report and help correct misclassifications, enabling ongoing model improvement. Developing clear guidelines for integrating the model's predictions into clinical workflows would be required, emphasizing its role as a supportive tool rather

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