The ability to detect biological damage caused by radiation exposure rapidly is a core part of Chemical, Biological, Radiological, and Nuclear (CBRN) defence readiness. Internationally, dicentric chromosomes (DCs) resulting from the misrepair of double-stranded DNA breaks are the most accepted, dependable cytogenetic biomarker of ionizing radiation exposure. However, existing automated approaches are limited by a lack of interpretability, dependence on end-to-end architectures, and insufficient robustness for real-world deployment. Still, the conventional Dicentric Chromosome Assay (DCA) is labor intensive, qualitative, and impracticable for high-volume triage applications in battlefield or nuclear accident scenarios. In this paper we present a two-stage deep learning pipeline that automates cytogenetic biodosimetry and is relevant for defence applications. In the first stage, object detection using a YOLOv8 model located individual chromosomes robustly from metaphase spreads. In the second stage, a lightweight ResNet18 classifier determined whether cropped chromosomes were normal or dicentric. Overall, the pipeline was trained on a curated dataset of 325 metaphase plates (~13,000 chromosomes). The results of the automated pipeline were validation outcomes listed as follows 0.92 precision, recall of 0.94, mAP@0.5 of 0.95, and overall accuracy classification of 94.2%. Our proposed system tremendously decreases the analysis time from hours to seconds, which enables rapid triage and diagnosis of radiation exposure for defence personnel and, by extension, civilian populations. The entire analysis pipeline is modular and can be deployed flexibly on lightweight hardware, which is ideal for use in, but not limited to, field-laboratories, mobile biodosimetry units, and hospitals. This work can be translated AI-driven cytogenetics into defence biosurveillance and can provide a deployable solution for live & accurate monitoring in the incident of nuclear accidents, radiological terrorism or in the event of radiation exposure on the battlefield.