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- Title
DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.
- Authors
Rana, Shakil; Hosen, Md Jabed; Tonni, Tasnim Jahan; Rony, Md. Awlad Hossen; Fatema, Kaniz; Hasan, Md. Zahid; Rahman, Md. Tanvir; Khan, Risala Tasin; Jan, Tony; Whaiduzzaman, Md
- Abstract
Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.
- Subjects
LUNGS; CONVOLUTIONAL neural networks; GRAPH neural networks; LUNG diseases; IMAGE recognition (Computer vision); NOSOLOGY; FEEDFORWARD neural networks; IDENTIFICATION
- Publication
Sensors (14248220), 2024, Vol 24, Issue 9, p2830
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s24092830