# NeuroMorphFusion: A Neuro-Inspired Hybrid Learning Framework for Interpretable Deep Lesion Detection in IoT-Enabled Healthcare Systems

## TL;DR

NeuroMorphFusion is a new framework for detecting lesions in medical imaging that combines brain-inspired learning with efficient computation for use in healthcare IoT systems.

## Contribution

The novel framework integrates spiking neural networks, morphological attention, and genetic algorithm optimization for interpretable and efficient lesion detection.

## Key findings

- NeuroMorphFusion achieves 98.18% classification accuracy on the IQ-OTHNCCD lung cancer CT dataset.
- The framework outperforms VGG16, SqueezeNet, MobileNetV3, and ResNet18 in transparency and efficiency.
- Optimization converges within eight minutes on a Jetson Nano, balancing accuracy, latency, and explainability.

## Abstract

Integrating deep learning within the Internet of Medical Things (IoMT) has revolutionized automated lesion detection in medical imaging. Yet, maintaining high diagnostic accuracy, interpretability and computational efficiency on resource-limited edge devices remains challenging. To address these gaps, we propose NeuroMorphFusion, a neuro-inspired hybrid framework that combines biologically plausible learning with mathematical modelling for interpretable and efficient lesion detection.

NeuroMorphFusion integrates a lightweight ResNet18 backbone, a Spiking Neural Network (SNN) component to capture temporal dynamics, and a morphological attention mechanism that emphasizes structure-relevant regions in CT scans. The architecture employs a semi-supervised reinforcement learning strategy, where pseudo-label accuracy and the overlap between Grad-CAM visualizations and expert annotations define the reward, ensuring explainable updates under limited labelled data. Additionally, a genetic algorithm (GA) optimizes hyperparameters—learning rate, dropout rate, spiking time steps, and attention dimensionality – for domain generalization and reduced memory use. The optimization population is restricted to 20 individuals over 30 generations, converging within eight minutes on a Jetson Nano.

A multi-objective optimization scheme balances lesion detection sensitivity, computational latency and explainability. Integrated SHAP and Grad-CAM visualizations enhance interpretability. Experimental evaluation on the IQ-OTHNCCD lung cancer CT dataset demonstrates that NeuroMorphFusion achieves 98.18% classification accuracy, outperforming VGG16, SqueezeNet, MobileNetV3, and ResNet18 in both transparency and efficiency.

NeuroMorphFusion effectively unites neuro-biological inspiration, mathematical interpretability, and edge-efficient computation for IoMT-based medical imaging. Its superior accuracy, explainability, and low-latency optimization highlight its potential for real-world clinical integration and scalable IoMT deployment.