Differential Diagnosis of Pulmonary Diseases using Convolutional Neural Network with LSTM Architecture

Pulmonary disorders, including conditions such as Pneumonia, Tuberculosis, and COVID-19, affect millions worldwide, presenting a major global health challenge. Accurate and timely detection of these diseases is crucial for effective treatment and patient care. This research paper introduces a novel CNN-LSTM model for effectively differentiating between and categorizing multiple lung disorders. Leveraging a dataset comprising 4529 chest X-rays (CXRs) collected from Shenzhen, Montgomery, Belarus, and COVID-19 Radiography datasets, the study aims to enhance disease diagnosis accuracy. Contrast-limited Adaptive Histogram Equalization and median filtering techniques have been applied to enhance image quality and reduce noise. Although LSTM networks are typically used for sequential data, this study employed them to capture spatial dependencies within 2D medical images. This approach allowed the model to learn complex representations and retain context from different image regions, enhancing feature extraction and overall performance. LSTM worked by exploiting the neighborhood relationships between pixels in CXRs by treating image patches as sequences. This approach enabled the model to better understand and utilize spatial information, thereby improving accuracy. Furthermore, baseline CNN and CNN-Bi LSTM models were also executed for a comparative study. Among the implemented models, CNN-LSTM emerged as the most effective, achieving an accuracy of 96.26% and precision, recall, F1 score of 96.44%, 96.62%, and 96.49%, respectively. It was followed by CNN-BiLSTM and CNN models with accuracy rates of 95.58% and 94.24%.