NUST Institutions Library Catalogue

Karyotyping continues to be the bedrock of cytogenetic diagnosis, providing key information
on chromosomal abnormalities causative of a broad range of genetic disorders, developmental
abnormalities, and cancers. But standard karyotyping is time-consuming, requires extensive
specialist interpretation, and is vulnerable to human mistake and inefficiency, especially in
high-volume clinical settings. Despite advances in medical image analysis, the automation of
karyotyping faces persistent challenges, including the lack of large-scale annotated datasets,
difficulties in segmenting overlapping chromosomes, and variability in chromosome
morphology and staining. These challenges define a significant research gap in developing
scalable, accurate, and clinically deployable deep learning models for automated chromosome
analysis.
In order to overcome this gap, we first present a large-scale, clinically annotated cytogenetic
database, built from 1,311 patients and consisting of 10,057 karyograms with 514,949
manually annotated chromosome singlets. Furthermore, 3,935 metaphase images are annotated
at the instance level in COCO format. This data set reflects true-world diversity, ranging from
normal and abnormal karyotypes to different Giemsa (G-banding) staining intensities,
structural abnormalities, and overlapping difficult cases, and thus presents a solid basis for the
creation and testing of deep learning models within automated cytogenetics.
Based on this work, we create two primary methodologies aimed at the fundamental tasks of
karyotyping. For segmentation of chromosomes, we introduce a variant Mask R-CNN model
involving an Attention-based Feature Pyramid Network (AttFPN), spatial attention, and a
LastLevelMaxPool component to improve multi-scale feature representation and contextual
perception. It enhances performance in difficult situations, including overlapping
chromosomes and weak banding patterns, and gains considerable improvements in mean
Average Precision (mAP) compared to standard baselines.
For chromosome classification, we present the Dual Attention Multiscale Pyramid Network
(DAMP), a specifically designed model that combines channel and spatial attention
mechanisms to concentrate on discriminative features, as well as a multiscale pyramid
architecture to cope with size, orientation, and quality variation in chromosomes. DAMP's
highest classification accuracy is 96.76% on both public and commercial datasets, performingxv
better than state-of-the-art models like ResNet-50, Vision Transformers, and Siamese
Networks.
Overall, this thesis provides interpretable and scalable deep learning models for automating
chromosome classification and segmentation. Through the closure of key gaps in dataset
quality, model resilience, and clinical utility, the work facilitates the insertion of clever
decision-support systems into cytogenetic pipelines, ultimately leading to improved diagnostic
reliability and efficiency in the face of chromosomal disorders.