Aycromo: An Open-Source Platform for Automatic Chromosome Detection in Metaphase Images Based on Deep Learning
2026-04-27 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created Aycromo, a simple computer program that helps doctors analyze chromosomes more quickly using artificial intelligence. This tool allows experts to load and test different AI models, see how well they work, and fix any mistakes easily without needing complicated commands. Their tests show that Aycromo can analyze chromosome images much faster while keeping high accuracy. This makes chromosome analysis less time-consuming and easier for medical use.
chromosome analysiskaryotypingcytogeneticsdeep learningYOLOv11metaphase imagesmAP@50ONNX RuntimeElectron frameworkAI-assisted diagnosis
Authors
Jorge L. A. Lima, Filipe R. Cordeiro
Abstract
Chromosome analysis is a fundamental step in the diagnosis of genetic diseases, but the manual karyotyping workflow is time-consuming and heavily dependent on expert specialists, often requiring several days per patient. Although Deep Learning models have achieved high performance in chromosome detection, most proposed solutions remain restricted to research prototypes or lack graphical interfaces suitable for clinical use. In this work, we present Aycromo, an open-source desktop platform for AI-assisted cytogenetic analysis. Built on Electron and ONNX Runtime, the tool allows cytogeneticists to load pre-trained models, compare architectures through an integrated benchmarking module, and manually correct detections via an interactive annotation interface, all without command-line interaction. Preliminary experiments on metaphase images from the CRCN-NE dataset demonstrate that YOLOv11 achieves 99.40% mAP@50, while the platform reduces per-slide analysis to seconds