Enhancing Pneumonia Diagnosis with Transfer Learning: A Deep Learning Approach
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 2
Abstract
The significant impact of pneumonia on public health, particularly among vulnerable populations, underscores the critical need for early detection and treatment. This research leverages the National Institutes of Health (NIH) chest X-ray dataset, employing a comprehensive exploratory data analysis (EDA) to examine patient demographics, X-ray perspectives, and pixel-level evaluations. A pre-trained Visual Geometry Group (VGG) 16 model is integrated into the proposed architecture, emphasizing the synergy between robust machine learning techniques and EDA insights to enhance diagnostic accuracy. Rigorous data preparation methods are utilized to ensure dataset reliability, addressing missing data and sanitizing demographic information. The study not only provides valuable insights into pneumonia-related trends but also establishes a foundation for future advancements in medical diagnostics. Detailed results are presented, including disease distribution, model performance metrics, and clinical implications, highlighting the potential of machine learning models to support accurate and timely clinical decision-making. This integration of advanced technologies into traditional healthcare practices is expected to improve patient outcomes. Future directions include enhancing model sensitivity, incorporating diverse datasets, and collaborating with medical professionals to validate and implement the system in clinical settings. These efforts are anticipated to revolutionize pneumonia diagnosis and broader medical diagnostics. This work offers comprehensive code for developing and optimizing deep learning (DL) models for medical image classification, focusing on pneumonia detection in X-ray images. The code outlines the construction of the model using pre-trained architectures such as VGG16, detailing essential preparation steps including image augmentation and metadata parsing. Tools for data separation, generator creation, and callback training for monitoring are provided. Additionally, the code facilitates performance assessment through various metrics, including the receiver operating characteristic (ROC) curve and F1-score. By providing a systematic framework, this research aims to accelerate the development process for researchers in medical image processing and expedite the creation of accurate diagnostic tools.
Authors and Affiliations
Rashmi Ashtagi, Nitin Khanapurkar, Abhijeet R. Patil, Vinaya Sarmalkar, Balaji Chaugule, H. M. Naveen
Keywords
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- EP ID EP744289
- DOI https://doi.org/10.56578/ida030203
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