Cutting-edge multi-task model: unveiling COVID-19 through fusion of image processing algorithms

• Published in: Computer methods in biomechanics and biomedical engineering. Vol. 12; no. 1
• Main Authors: Kordnoori, Shirin, Sabeti, Maliheh, Mostafaei, Hamidreza, Seyed Agha Banihashemi, Saeed
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 31.12.2024; Taylor & Francis Group
• Subjects: Covid-19 diagnosis; image enhancement; Medical image analysis; multi-task learning
• ISSN: 2168-1163; 2168-1171
• DOI: 10.1080/21681163.2023.2287521

The COVID-19 pandemic underscores the vital need for accurate lung infection diagnosis to guide effective medical interventions. In response, this research introduces a novel deep multi-task model that seamlessly integrates segmentation and classification tasks for the detection of COVID-19 in CT scan images. This innovative model leverages a shared encoder for feature extraction, a dedicated decoder for segmentation, and a multi-layer perceptron for classification. The primary objective of this model is to address the challenge of task imbalance introduced by the application of image processing algorithms in the multi-task models. Our study involves a two-stage evaluation. Initially, we apply the proposed multi-task model with image processing algorithms to highlight task imbalance. Subsequently, we balance tasks by combining binary image processing algorithms. Evaluation on four datasets shows impressive results with a Dice coefficient of 88.91 ± 0.01 for segmentation and 0.97 classification accuracy. In summary, this model advances medical image analysis for enhanced diagnostic precision in healthcare.