## 3 Results
### DL Methods  
Deep learning approaches for NPH detection generally operate without explicitly defined features, instead automatically extracting relevant patterns from imaging data. [PERSON] et al. developed a fully automated 3D DCNN using whole-brain T1-weighted MRI from a balanced dataset of 23 NPH patients, 23 AD patients, and 23 controls [29]. Their approach achieved high diagnostic accuracy of 0.90, successfully classifying 21/23 NPH cases, 19/23 AD cases, and 22/23 controls. The model's sensitivity and specificity for NPH were both 0.91, demonstrating balanced performance across diagnostic categories. Their innovative use of Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the model focused on diagnostically relevant regions: the brain parenchyma surrounding the lateral ventricle for NPH cases and the medial temporal lobe for AD cases. This alignment between the model's attention areas and known disease-specific regions provides interpretability and validates the biological relevance of the DL approach. Architecture Types  
1. Convolutional Neural Networks  
While [28] aimed to classify among three neurodegenerative disorders, [45] focused more on obtaining or classifying distinct types of NPH, including NPH. They aimed to use DL algorithms to eliminate steps like pre-processing, segmentation, feature extraction, and classification. A color-based transformation technique is used for better processing of input tested images. Then, these pre-processed images are segmented by mean shift clustering, which is used to segment the image and provide a reliable and accurate estimated value. Then the features are extracted using Complete Local Binary Pattern. Finally, classification used DCNN with Emperor Penguin Optimization (EPO) for improving the system efficiency, with this optimization step being an additional step in the way of DL. The developed model achieves an accuracy of approximately 99.1%, sensitivity of approximately 98.5%, and precision value of approximately 98.2%, respectively. Additionally, the average training and validation accuracy of the system is 84.75% and 87.25%, to differentiate between the different types of hydrocephalus. Reference [47] focused on developing an automated deep learning system to diagnose different hydrocephalus conditions. The study presented promising results in terms of classification performance and effectiveness. We found that 3D CNN is commonly used in the field. The study by [PERSON] et al. analyzed the relationship between cerebrospinal fluid variation and NPH in patients with different brain injuries. They used a 3D DCNN approach for pattern recognition and image classification. They collected MRIs from different brain damaged patients. A DL-based DCNN model was used to preprocess the image features, and the offline training and online reconstruction were conducted after the construction of the model. Four databases were selected for deep learning and analyzed using principal component analysis and 3D scale-invariant feature transformation. The weighted histogram of gradient orientation descriptor and ROC-AUC scores were the highest. Scale-invariant feature transformation, principal component analysis, and weighted histogram of gradient orientation had sensitivities of 87.5%, 88.2% and 90.1%, respectively, and specificities of 91.8%, 90.1%, and 94.2%, respectively. weighted histogram of gradient orientation also had the highest correct rate at 92.4%. The CSF volume in the subarachnoid space was 77.04% higher than that in the ventricles [45]. 3D CNNs offer significant advantages over 2D approaches for NPH diagnosis by capturing the full volumetric context of brain structures. Unlike 2D CNNs that process slices independently, 3D CNNs simultaneously model spatial relationships in all three dimensions (axial, sagittal, and coronal), preserving critical volumetric information about ventricular morphology and CSF distribution.