Chimp-Optimized Latent Diffusion and Vision Transformer Architecture for High-Resolution Chromosome Morphological Segmentation and Chromosomal Aberration Detection

Abstract

Background: Accurate identification of chromosomal abnormalities is fundamental to clinical cytogenetics, molecular diagnostics, and precision medicine. However, conventional automated karyotyping systems often encounter challenges such as image noise, chromosome overlap, low contrast, and subtle structural abnormalities, leading to reduced diagnostic accuracy.

Methods: To address these limitations, this paper proposes a novel Chimp-Optimized Latent Diffusion and Vision Transformer (COA-LDViT) framework for high-resolution chromosome morphological segmentation and chromosomal aberration detection. Initially, cytogenetic microscopy images are enhanced using a Latent Diffusion Model (LDM) to perform image super-resolution, denoising, and artifact removal while preserving fine chromosomal structures. The enhanced chromosome images are subsequently processed using a Shifted-Window Vision Transformer (Swin-ViT) to achieve accurate pixel-level semantic segmentation and chromosome boundary localization. To further improve model performance, a Chimp Optimization Algorithm (COA) is employed to optimize transformer hyperparameters, including attention heads, patch embedding dimensions, learning rate, and network depth, thereby accelerating convergence and improving segmentation accuracy.

Results: The proposed COA-LDViT framework is evaluated on publicly available clinical cytogenetic chromosome datasets using performance metrics such as mean Intersection-over-Union (mIoU), Dice Similarity Coefficient (DSC), precision, recall, and F1-score. Experimental results demonstrate that the proposed framework significantly outperforms existing deep learning approaches in chromosome segmentation and structural aberration detection.

Conclusion: The proposed intelligent framework provides an efficient, automated, and high-throughput solution for chromosome karyotyping, chromosomal abnormality analysis, and molecular cytogenetic research.