## 4 Discussion  
Bilirubin-induced brain injury is distinctive, with the pallidum, basal ganglia, substantia nigra, hippocampus, thalamic nuclei, and putamen being notably and selectively affected in a symmetrical pattern [9; 13; 29]. The most common and characteristic finding in ABE is bilateral symmetric high signals on T1-weighted MRI. In cases of severe neurological damage caused by bilirubin, MRI can, to some extent, detect signal changes caused by the injury. This change is a long-term progression, characterized by bilateral symmetric high signals in T1-weighted sequences transitioning to high signals in T2-weighted sequences and fluid-attenuated inversion recovery (FLAIR) sequences in specific areas of the brain [10; 13; 30]. However, not all patients with hyperbilirubinemia exhibit typical radiological findings that are visible to the naked eye in conventional MRI sequences commonly used in current clinical practice. This is particularly true for patients who are not critically ill or are in the early stages of the condition. Identifying ABE or hyperbilirubinemia using MRI based on traditional radiological experience is quite challenging in clinical practice. In this study, we proposed a deep learning framework based on multi-modal feature fusion and attention modules to distinguish ABE from non-ABE conditions in newborns. The application of the attention module and CentralNet has demonstrated the potential for advanced diagnostic accuracy in the evaluation of MRI data. The attention module's ability to refine the focus on salient features within the imaging data is evidenced by the observed improvements in sensitivity and precision scores. Furthermore, the incorporation of CentralNet has notably enhanced the model's accuracy and AUC. This is largely attributed to CentralNet's ability to effectively uncover inter-modal associative information. During the feature extraction phase, there is mutual supervision and learning across modalities, thereby more efficiently distilling critical diagnostic information from the complex imaging data. This collaborative process facilitated by CentralNet leads to a more insightful synthesis of diagnostic features, improving the model's overall diagnostic capability. Although the specificity did not improve at the same rate, it remained relatively consistent, which implies that the models did not lose their ability to identify true negatives correctly. This is particularly important in clinical practice to avoid false positives that could lead to unnecessary interventions. The notable increase in the F1 score with the use of both the attention module and CentralNet indicates a balanced improvement in the precision and recall, which is critical for the reliable classification of medical imaging data. However, our study still has certain limitations. First, the source of our patient sample is singular and the sample size is relatively small. Although we have employed different \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Procedure** & **None (Early Fusion)** & **None (Later Fusion)** & **AM (Later Fusion)** & **Ours Model** \\ \hline Preprocessing & 59.0 (s/sample) & 59.0 (s/sample) & 59.0 (s/sample) & 59.0 (s/sample) \\ Training & 2.10 (s/epoch) & 2.20 (s/epoch) & 3.80 (s/epoch) & 4.50 (s/epoch) \\ Testing & 0.25 (s/epoch) & 0.27 (s/epoch) & 0.35 (s/epoch) & 0.60 (s/epoch) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative processing times in different computational models. strategies, such as data augmentation and transfer learning, to address overfitting, these measures may not completely offset the effects of the small sample size, which could affect the model's ability to generalize. This limitation may also impede the attainment of a consistent sample distribution across training, validation, and test datasets. Secondly, as illustrated in Table 1, there is a notable age difference between subjects in the ABE group and those in the non-ABE group.