A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases

Authors

Santosh Kumar, IIIT Naya Raipur
Vijesh Bhagat, IIIT Naya Raipur
Prakash Sahu, IIIT Naya Raipur
Mithliesh Kumar Chaube, IIIT Naya Raipur
Ajoy Kumar Behera, All India Institute of Medical Sciences, Raipur
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Raffaele Gravina, Università della Calabria
Michele Di Dio, Università della Calabria
Giancarlo Fortino, Università della Calabria
Edward Curry, University of Galway
Saeed Hamood Alsamhi, University of Galway

Document Type

Article

Publication Title

Computer Methods and Programs in Biomedicine

Abstract

Background and objective: Chronic Obstructive Pulmonary Disease (COPD) is one of the world's worst diseases; its early diagnosis using existing methods like statistical machine learning techniques, medical diagnostic tools, conventional medical procedures, and other methods is challenging due to misclassification results of COPD diagnosis and takes a long time to perform accurate prediction. Due to the severe consequences of COPD, detection and accurate diagnosis of COPD at an early stage is essential. This paper aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients based on prepared Computerized Tomography (CT) scan images and lung sound/cough (audio) samples using machine learning techniques, which are presented in this study. Method: The proposed multimodal framework extracts texture, histogram intensity, chroma, Mel-Frequency Cepstral Coefficients (MFCCs), and Gaussian scale space from the prepared CT images and lung sound/cough samples. Accurate data from All India Institute Medical Sciences (AIIMS), Raipur, India, and the open respiratory CT images and lung sound/cough (audio) sample dataset validate the proposed framework. The discriminatory features are selected from the extracted feature sets using unsupervised ML techniques, and customized ensemble learning techniques are applied to perform early classification and assess the severity levels of COPD patients. Results: The proposed framework provided 97.50%, 98%, and 95.30% accuracy for early diagnosis of COPD patients based on the fusion technique, CT diagnostic model, and cough sample model. Conclusion: Finally, we compare the performance of the proposed framework with existing methods, current approaches, and conventional benchmark techniques for early diagnosis.

DOI

10.1016/j.cmpb.2023.107911

Publication Date

1-2024

Keywords

Deep learning, Chronic Obstructive Pulmonary Disease (COPD), Computed Tomography (CT), Texture, Lung sound, Classification

Comments

IR conditions: non-described

Recommended Citation

S. Kumar et al. "A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases," Computer Methods and Programs in Biomedicine, vol. 243, Jan 2024. doi: 10.1016/j.cmpb.2023.107911