Deep Learning and Genetic Disorder Detection: A Dual Approach to Detect Sickle Cell and Cystic Fibrosis
M R Ankitha · International Journal for Research in Applied Science and Engineering Technology · 2025
Automated screening of genetic blood disorders like Sickle Cell Disease (SCD) and Cystic Fibrosis (CF) can greatly augment screening in low-resource environments. We present a hybrid deep-learning architecture of classification (CNN) and object detection (YOLOv3) to screen microscopic images and medical scans to detect these diseases. The pipeline utilizes preprocessed, labeled blood-smear images to detect abnormal erythrocytes and classify cell morphology. We further incorporate hybrid classifiers (Random Forest, SVM, Deep Neural Networks) on convolutional features to enhance accuracy. Using public blood-cell datasets (e.g. BCCD and ErythrocytesIDB) and simulated clinical CF scans, our results exhibit high accuracy (>98%) to distinguish sickled vs. normal red blood cells. Embedded device implementations (e.g. smartphone or Raspberry Pi microscopes) are demonstrated for cost-effective deployment. Results demonstrate that the YOLOv3+CNN hybrid method can match or surpass human-level performance in automated screening, paving the way for scalable, cost-effective diagnostic equipment in clinical practice.