LungXpertAI: A deep multi-task learning model for chest CT scan analysis and COVID-19 detection.

• Medical imaging plays an important role in the diagnosis of COVID-19, and intelligent methods, including image processing algorithms, have shown success in diagnosing pneumonia. • Multi-task learning simultaneously addresses multiple related tasks, utilizing shared representations to overcome the limitations of labeled data in medical image analysis. • Spatial attention mechanisms, like the Convolutional Block Attention Module (CBAM), enhance feature extraction in multi-task models, improving the precision of diagnosis and treatment decisions. • Conditional Random Fields (CRF) post-processing refines segmentation outcomes by accounting for spatial dependencies, enhancing the precise delineation of COVID-19-affected regions in CT scans. • The LungXpertAI model integrates multi-task learning, advanced image processing techniques, attention mechanisms, and post-processing using CRF, delivering a robust and optimized solution for accurate and efficient COVID-19 analysis from chest CT scans. Addressing the urgent need for accurate COVID-19 identification and lung infection segmentation in CT scans, our study introduces LungXpertAI, a novel Multi-Task Learning architecture. Previous studies on multi-task structures have been limited in providing detailed insights into their architectures. Addressing these details is crucial for enhancing the performance of multi-task structures. Our proposed multi-task structure, based on the U-Net architecture, incorporates a shared encoder, a specialized segmentation decoder, and a multi-layer perceptron for classification. In the proposed approach, image processing algorithms, including histogram equalization, median filtering, and morphological operations have been employed in the pre-processing stage of input images. Also, by combining these algorithms in pairs, an attempt has been made to enhance the performance of tasks. Initial preprocessing and the integration of the Convolutional Block Attention Module (CBAM) contribute to effective feature extraction. Post-processing with Conditional Random Field (CRF) further refines segmentation outputs. CRF considers spatial dependencies within the segmentation results, contributing to more precise delineation of COVID-19-affected areas in CT scans. Evaluation on using dataset demonstrates the effectiveness of pairwise combinations of image processing algorithms, achieving superior results with a segmentation accuracy of 95.66 % and a classification accuracy of 95.62 %. The incorporation of the CBAM module improves classification accuracy to 96.22 %. Moreover, Integrating CBAM and CRF significantly improves segmentation accuracy to 96.51 %. The application of our proposed method steps to U-Net++ and ResUnet showcases their potential for enhancing multi-task structures. This study establishes new COVID-19 detection standards, promising progress in medical image analysis. [ABSTRACT FROM AUTHOR]