Cases

Diagnosis of brain tumors using neural networks

Smart healthcare
Issue: Brain tumors are prevalent and aggressive diseases affecting both children and adults, constituting 85 to 90 percent of all primary Central Nervous System (CNS) tumors. Annually, approximately 12,000 individuals receive a brain tumor diagnosis.

Objective: Develop a system capable of accurately detecting and classifying brain tumor image data to mitigate human errors and aid radiologists in interpreting Magnetic Resonance Imaging (MRI) results.

Product: Introducing a Neural Network Predictive System leveraging computer vision technology to analyze MRI images in real-time.

Solution: Our team has engineered a cutting-edge system designed to assist medical professionals, particularly radiologists, in preemptively identifying brain tumors based on MRI findings. This innovation not only enhances their efficiency but also expands the capacity for patient care. Achieving a model accuracy exceeding 90%, our solution ensures reliable diagnosis.

Market Advantage: By reducing the manual examination duration for radiologists and eliminating the potential for human error in MRI result interpretation, our system enhances diagnostic accuracy and expedites patient care processes.