| Autor: |
Kordnoori, Shirin, Sabeti, Maliheh, Mostafaei, Hamidreza, Seyed Agha Banihashemi, Saeed |
| Zdroj: |
Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation; Dec2024, Vol. 12 Issue 1, p1-10, 10p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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