Contextual Corpus Callosum Analysis for Differentiating Early and Late Mild Cognitive Impairment

In recent times, deep learning extensively utilized across diverse domains, including Radiology. They play a pivotal role in early disease detection by analyzing data to identify patterns and biomarkers. Here, we propose a novel approach for classifying between Early MCI (EMCI) and Late MCI (LMCI), by integrating object detection with 3D CNN. Leveraging the capabilities of YOLOv5 object detection and 3D CNN, we focus on identifying changes in the corpus callosum (CC) and its neighbourhood, a crucial brain structure facilitating inter-hemispheric communication. Unlike existing methods that require manual cropping of CC, our approach integrates both detection and classification models, along with eliminating need for preprocessing steps. Applying on balanced dataset of Structural MRI volumes of 1098 subjects obtained from publicly available ADNI dataset, our method achieves accuracy of 88.41%. The novelty lies in integrating YOLOv5 object detection with 3D CNN for complete automation without the need for preprocessing, thereby utilizing "CC with context" for classification. The importance of utilizing neighbourhood of CC is illustrated by enhance classification accuracy of nearly 7%.Clinical relevance- This study demonstrates that the proposed 3D CNN model can accurately differentiate between early and late stages of Mild Cognitive Impairment by focusing on distinct subregions of the CC, aligning with known patterns of neurodegeneration. The model's interpretability to localize clinically relevant brain regions enhances its diagnostic value and supports more targeted, stage-specific interventions.