A Tumor MRI Image Segmentation Framework Based on Class-Correlation Pattern Aggregation in Medical Decision-Making System

Hui Wei, Baolong Lv, Feng Liu, Haojun Tang (), Fangfang Gou and Jia Wu
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Hui Wei: School of Modern Service Management, Shandong Youth University of Political Science, Jinan 250102, China
Baolong Lv: School of Modern Service Management, Shandong Youth University of Political Science, Jinan 250102, China
Feng Liu: School of Information Engineering, Shandong Youth University of Political Science, Jinan 250102, China
Haojun Tang: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Fangfang Gou: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Jia Wu: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Mathematics, 2023, vol. 11, issue 5, 1-25

Abstract: Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the interaction of global semantic information. Second, although medical image pattern recognition can learn representative semantic features, it is challenging to ignore useless features in order to learn generalizable embeddings. Thus, a tumor-assisted segmentation method is proposed to detect tumor lesion regions and boundaries with complex shapes. Specifically, we introduce a denoising convolutional autoencoder (DCAE) for MRI image noise reduction. Furthermore, we design a novel tumor MRI image segmentation framework (NFSR-U-Net) based on class-correlation pattern aggregation, which first aggregates class-correlation patterns in MRI images to form a class-correlational representation. Then the relationship of similar class features is identified to closely correlate the dense representations of local features for classification, which is conducive to identifying image data with high heterogeneity. Meanwhile, the model uses a spatial attention mechanism and residual structure to extract effective information of the spatial dimension and enhance statistical information in MRI images, which bridges the semantic gap in skip connections. In the study, over 4000 MRI images from the Monash University Research Center for Artificial Intelligence are analyzed. The results show that the method achieves segmentation accuracy of up to 96% for tumor MRI images with low resource consumption.

Keywords: MRI tumor segmentation; intelligent assisted diagnosis; classification; class-correlational representation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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