Advancing Diagnostic Accuracy in Lung Disease Severity Classification Using Multi-Domain Features
Urvashi Deshmukh1, Prapti Deshmukh2
1Urvashi Deshmukh, Scholar, Department of Computer Science, Dr. G. Y. Pathrikar College of CS and IT, MGM University, Aurangabad (Maharashtra), India.
2Prapti Deshmukh, Department of Computer Science, Dr. G. Y. Pathrikar College of CS and IT, MGM University, Aurangabad (Maharashtra), India.
Manuscript received on 26 March 2025 | First Revised Manuscript received on 06 April 2025 | Second Revised Manuscript received on 17 June 2025 | Manuscript Accepted on 15 July 2025 | Manuscript published on 30 July 2025 | PP: 8-17 | Volume-5 Issue-5, July 2025 | Retrieval Number: 100.1/ijpmh.D107805040525 | DOI: 10.54105/ijpmh.D1078.05050725
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Accurate classification of lung diseases is crucial for early diagnosis and effective treatment. This study presents an optimised classification framework that utilises multi-domain feature extraction and a deep neural network (DNN) for categorising lung disease severity from CT scan images. The dataset collected from a local hospital includes 266 CT scans of Lung cancer, COVID-19, and pneumonia, categorized into mild (43 images), moderate (82 photos), and severe (141 images) cases. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied, ensuring equal representation across categories. A total of 30 multi-domain features were extracted using a comprehensive feature extraction methodology that combined wavelet packet decomposition (WPD) with statistical, texture, shape, edge detection and Grey Level Cooccurrence Matrix (GLCM) features. These features captured diverse spatial and frequency-based characteristics of lung disease patterns, enabling robust model input. This study focuses on classification based on the severity of patient condition within three different classes: Mild, Moderate, and Severe, related to lung disease. The classification was performed using a Deep Neural Network (DNN) with fine-tuned hyperparameters. The model achieved a training accuracy of 95%. The findings underline the potential of this approach in improving automated diagnostic systems. The extracted features provide a comprehensive representation of disease patterns, while the DNN leverages these features for precise classification. This methodology offers valuable insights for applications in medical imaging. This research contributes to the field of medical image analysis by integrating robust feature extraction techniques with advanced classification models, paving the way for more accurate and reliable lung disease diagnosis.
Keywords: Synthetic Minority Oversampling Technique, Wavelet Packet Decomposition, Grey Level Co-occurrence Matrix, Deep Neural Network.
Scope of the Article: Community Health