MCLSG:Multi-modal classification of lung disease and severity grading framework using consolidated feature engineering mechanisms
Abobaker Mohammed Qasem Farhan, Shangming Yang, Abdulrahman Q.S. Al-Malahi, Mugahed A. Al–antari
Abstract
Human disease detection using medical images with algorithmic severity prediction is unexplored. Deep learning algorithms for disease identification and classification from medical images have been proposed recently. Precision medicine requires more than illness detection. Expert diagnosis and advanced medical examinations are frequently necessary to decrease the likelihood of cross-infection and cater to a patient based on his severity level. To enable the assessment of human disease detection with its severity, we propose the Multi-modal Classification of Lung Disease and Severity Grading (MCLSG) framework from the different medical image modalities using dedicated feature engineering mechanisms. The automatic MCLSG framework consists of pre-processing, automatic feature extraction, classification, and severity analysis. The goal of pre-processing phase is to enhance the image quality and localize the region of interest (ROI) without using any segmentation method. The unwanted image regions are cropped followed by the quality enhancement in the pre-processing. In the automatic feature extraction phase, the optimized Convolutional Neural Network (CNN) layers perform the automatic feature learning and selection using pre-trained parameters. Optimized CNN is then combined with machine-learning classifiers for disease identification. When the disease is detected, MCLSG does automatic disease severity assessments utilizing a consolidated feature engineering technique that includes high-level and low-level characteristics rather than only CNN features. According to the outcome of consolidated feature engineering, we divide patients into four severity levels (mild, moderate, severe, and fatal). The experimental findings show that the proposed MCLSG model is efficient. Overall results show that MCLSG improves the accuracy by 3.5 % for disease detection and 6.8 % for severity level analysis.