Classification and Segmentation of Medical Images Using Cross-Representation Attention Fusion and Fuzzy Image...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Accurate classification and segmentation of medical images are crucial for diagnosis.
• Existing methods often struggle with complex image features and require separate models for different tasks.

Purpose of the Study:

• To develop a unified framework for joint classification and segmentation of chest X-ray and kidney images.
• To enhance feature representation and fusion for improved diagnostic performance.

Main Methods:

• Proposed a Cross-Representation Attention-Based Neural Network incorporating fuzzy image enhancement.
• Generated three complementary image representations (histogram spread, fuzzy entropy, fuzzy standard deviation).
• Employed a Cross-Representation Attention Fusion module for adaptive feature integration.

Main Results:

• The proposed method outperformed conventional and baseline models in both classification and segmentation tasks.
• Ablation studies confirmed the contributions of fuzzy enhancement, cross-representation attention, and multi-task learning.
• Demonstrated stability and ability to localize relevant lesion regions in chest X-rays and kidney images.

Conclusions:

• The framework offers an effective and interpretable approach for unified medical image analysis.
• Achieved a balance between predictive performance and computational cost.
• Highlights the potential of cross-representation attention and fuzzy enhancement in medical image diagnostics.