Abstract

Medical image fusion has played a critical role in clinical diagnosis by integrating complementary information from multi modal sources such as MRI, CT, and PET. Conventional fusion techniques have suffered from information loss, spectral distortion, and weak adaptability under complex anatomical variations. Recently, deep learning and fuzzy inference approaches have emerged as promising solutions, yet they have remained sensitive to parameter initialization and local optima. Existing deep neuro-fuzzy fusion models have exhibited limited robustness due to static membership functions and suboptimal rule optimization. These limitations have resulted in blurred edges, reduced contrast preservation, and unstable fusion quality across heterogeneous imaging modalities. The lack of adaptive optimization has restricted their generalization in real clinical environments. This work has proposed a swarm-enhanced deep neuro-fuzzy system for multi modal medical image fusion. A deep neuro-fuzzy architecture that has integrated convolutional feature extraction with fuzzy inference has been developed. Swarm intelligence that has included particle-based optimization has been employed to adaptively tune fuzzy membership parameters and rule weights. Feature learning that which has captured spatial and textural cues has been followed by a fuzzy decision layer that which has modeled uncertainty and nonlinearity. The fusion strategy has combined salient features using optimized fuzzy rules, while reconstruction that which has preserved anatomical consistency has been performed. Experimental evaluations are conducted on standard multi modal medical image datasets. The proposed system achieves higher entropy (up to 7.11), structural similarity index (up to 0.94), edge preservation index (up to 0.88), peak signal-to-noise ratio (up to 33.8?dB), and mutual information (up to 2.68) compared with conventional deep learning and fuzzy-based fusion methods. Visual analysis demonstrates that clinically relevant structures are better preserved while noise and artifacts are significantly reduced. The swarm optimization that which guides parameter learning improves convergence stability and fusion consistency across modalities.

Authors

Maram Ashok¹, Thamari Thankam²
Malla Reddy College of Engineering, India¹, Cihan University-Erbil, Iraq²

Keywords

Medical Image Fusion, Neuro-Fuzzy Systems, Swarm Optimization, Multi Modal Imaging, Deep Learning