HealthTech & Deep Learning
Healthcare AI Diagnostics
- Industry
- Healthcare
- Duration
- 3 Months
- Team Size
- 4 Members
- Platform
- Cloud / GPU Cluster
The Challenge
Early detection of neurodegenerative diseases is critical but manually analyzing MRI scans is time-consuming and prone to human error.
- Subtle structural changes are often missed in early stages.
- High volume of scans creates a bottleneck for radiologists.
- Need for consistent and reproducible diagnostic criteria.
- Integrating AI results into existing medical workflows.
Our Solution
Developed a specialized deep learning model trained on annotated MRI datasets to act as a diagnostic assistant.
- Custom YOLO model trained on a large dataset of MRI scans.
- Automated segmentation and highlighting of regions of interest.
- Confidence scoring system to prioritize critical cases.
- Seamless integration with medical imaging standards (DICOM).
Challenges We Overcame
- Overcoming class imbalance in medical datasets.
- Achieving high sensitivity without increasing false positives.
- Ensuring model explainability for clinical trust.
- Handling variations in MRI scanner outputs.
Technology Stack
Deep Learning
YOLO
Python
PyTorch
Medical Imaging
FastAPI
Results & Impact
- Increased diagnostic accuracy by 15% in early-stage cases.
- Reduced analysis time per scan from 30 minutes to under 2 minutes.
- Enabled earlier patient intervention strategies.
- Standardized the initial screening process across multiple clinics.
Project Gallery
Project Details
IndustryHealthcare
Duration3 Months
Team Size4 Members
PlatformCloud / GPU Cluster
Services Provided
DevelopmentDiagnostic Pipeline
AI/MLDeep Learning Models
ComplianceHIPAA Standards