In the UK, one woman is diagnosed with breast cancer every 9 minutes. Survival rate is 99% when caught early but drops to 26% when caught late. The gap between early and late detection is often access to fast, reliable diagnostic support.
DetectIQ is a machine learning dashboard that classifies tumours as Malignant or Benign using biopsy cell nucleus features. It is not designed to replace doctors, but to support faster, more informed clinical decisions.
The project has attracted attention from NHS-tagged organisations on LinkedIn and received endorsement from healthcare professionals including a Registered Nurse and a Clinical Officer who noted it "makes diagnosis faster."
MediPublish struggled to efficiently onboard and route medical publications to the correct clinical departments, with significant redundancy, class imbalance, and duplicate abstracts compounding the problem.
Fine-tuned Microsoft BioMedBERT — a transformer pretrained on biomedical text — to auto-classify medical abstracts across 5 disease departments: neoplasms, digestive, nervous system, cardiovascular, and general pathological conditions.
The weighted model (80% accuracy) outperforms the standard model (82%) for minority class recall, making it better suited for routing niche publications to specialised departments reliably.
Corrosion on metallic surfaces and underwater structures is a major safety and maintenance challenge. Manual inspection is costly, dangerous, and inconsistent.
A computer vision pipeline that classifies images as corrosion-positive or corrosion-negative using progressively advanced models.
Accurate and rapid assessment of cardiac valve state from ultrasound imaging is critical in clinical settings but requires expert interpretation.
Binary classification of cardiac ultrasound images to detect whether the mitral valve is open or closed, using a published clinical dataset from Cervantes-Guzmán et al. (2023).