Project Report: Multi-label Classification of Chest X-Rays

1. Introduction The aim of this project is to address the need for an accurate and timely interpretation of chest X-ray images to assist healthcare professionals in diagnosing and treating various lung diseases effectively. A chest X-ray is a common and cost-effective medical imaging test, but clinical chest X-ray diagnosis can be challenging. This project employs advanced multi-label classification techniques based on deep and machine learning to accurately classify and anticipate lung ailments from chest X-ray images.

2. Motivation The motivation behind this project is to improve chest X-ray diagnosis by utilizing artificial intelligence and deep learning algorithms. Clinical chest X-ray diagnosis can be difficult and sometimes more challenging than chest CT imaging diagnosis. By developing a robust AI-based chest X-ray prediction system, we aim to provide an economical and accurate solution for chest X-ray disease detection, allowing patients to identify lung diseases and take necessary precautions. Additionally, this project aims to extend its services to businesses and research sectors to aid in the development of next-generation radiology tools.

3. Problem Statement The primary problem addressed in this project is the need for accurate and timely interpretation of chest X-ray images to detect and classify various lung diseases. The goal is to assist healthcare professionals in diagnosing lung ailments effectively using automated AI-driven methods.

4. Solution The proposed solution involves the development of an AI-based chest X-ray prediction system utilizing deep learning algorithms to analyze and interpret chest X-ray images. The system is capable of detecting and classifying abnormalities, providing healthcare professionals with valuable insights for effective diagnosis and treatment. The key contribution of this research is the creation of a new hybrid deep learning algorithm suitable for predicting lung diseases from X-ray images.

5. Objectives The project aims to achieve the following objectives:

Develop a robust AI model to predict one of the nine lung diseases based on chest X-ray images. Enhance clinical decision-making by revolutionizing the interpretation of X-ray images through AI. Provide a cost-effective solution for accurate chest X-ray disease detection. Enable patients to identify the specific lung disease they are affected by and take necessary precautions. Extend services to businesses and research sectors to facilitate research in developing advanced radiology tools. 6. Methodology The project followed a structured approach, outlined as follows:

Data Collection: Obtained a dataset from the NIH Chest X-ray Dataset containing 30,805 distinct patients' 112,120 X-ray images labeled with diseases. Exploratory Data Analysis: Analyzed the dataset to understand its characteristics and distribution. Data Preprocessing: Cleaned and standardized the data, addressing class imbalance and ensuring uniformity. Data Splitting: Segmented the data into training, validation, and testing sets. Model Preparation: Developed various models including ResNetV50, DenseNet121, RandomForest, and DecisionTree. Hyperparameter Tuning: Optimized the models through hyperparameter tuning for improved performance.

7. Dataset Details Dataset: NIH Chest X-ray Dataset Number of Patients: 30,805 Number of X-ray Images: 112,120 Classes: 10 (including "No findings" and 9 specific lung diseases)

8. Results Successfully developed and optimized various deep learning models. Achieved reliable classification and forecasting of lung illnesses based on X-ray images. Generalized well to unseen X-ray images, providing reliable predictions.

9. Conclusion and Future Work In conclusion, this project demonstrates the potential of advanced deep learning techniques in accurately classifying and anticipating lung ailments from chest X-ray images. The developed AI model provides an economical and accurate solution for chest X-ray disease detection, benefiting both healthcare professionals and patients. Future work involves further refinement of the model, exploring new deep learning architectures, and collaborating with healthcare institutions to integrate the model into clinical practice.

For a demonstration of the deployed model, please reach out for further details.

Link to the EDA: https://colab.research.google.com/drive/1YBTifblElUtxjtHm2M7YqphkeUQx3kQu?usp=sharing#scrollTo=chaDesyyT7KW Link to the ML Models: https://www.kaggle.com/code/satyakichoudhury/notebook1f9a64f1c6/edit Link to the Various Output Graphs: https://www.kaggle.com/code/manojadithya/notebook1bbed49396