HybridCISN: Integrating 2D/3D convolutions and involutions with hyperspectral imaging and blood biomarkers for neonatal disease detection

Early detection and accurate diagnosis of neonatal diseases are crucial for improving health outcomes and reducing infant mortality. This study introduces a novel Hybrid Convolutional and Involutional Spectral Network (HybridCISN) that integrates hyperspectral imaging (HSI) data with blood biomarker analysis to enhance neonatal health diagnostics. By combining 2D convolution, 3D convolution, and involution layers, the HybridCISN model extracts spatial, spectral, and channel-specific features, addressing limitations in traditional diagnostic methods. The model was evaluated through two distinct approaches: (1) using only HSI spectral data and (2) integrating HSI spectral data with blood biomarkers such as haemoglobin and bilirubin levels. These approaches were tested for both binary classification (healthy vs. unhealthy neonates) and multiclass classification (specific neonatal diseases such as intracranial hemorrhage, necrotizing enterocolitis, pneumothorax, and respiratory distress syndrome). Experimental results demonstrate the HybridCISN model's superior performance, achieving an overall accuracy of 93.64% for binary classification and 90.25% for multiclass classification. Compared to state-of-the-art methods such as the involution-based HarmonyNet and the 2D/3D convolution-based HybridSN, the HybridCISN model achieved accuracy improvements of 0.8% and 1.5%, respectively, in multiclass classification. The second approach, integrating blood biomarkers, improved diagnostic sensitivity and specificity, emphasizing the value of multimodal data fusion. Involution layers reduced channel redundancy and optimized feature extraction, as confirmed by ablation studies. The HybridCISN model offers a scalable and non-invasive diagnostic framework, addressing clinical applicability and biomarker accessibility, while combining precision, efficiency, and robustness to advance neonatal disease detection and set a benchmark for future research in medical imaging.