Project Story: DeepMed Detect – AI for Abnormality Detection in Medical Imaging

What Inspired Us

In today’s fast-paced hospital environments, radiologists handle hundreds of scans daily. Minor anomalies like small tumors or fractures can be overlooked due to human fatigue or scan complexity. We were inspired by the idea of using artificial intelligence to support radiologists in identifying abnormalities early—making diagnosis faster, more accurate, and more accessible to everyone.

How We Built It

We developed three deep learning models using Convolutional Neural Networks (CNNs) trained on publicly available datasets for:

Brain Tumor Detection (MRI)
Lung Tumor Detection (CT scans)
Bone Fracture Detection (X-ray)

Our pipeline included:

Image preprocessing and resizing
Data augmentation for generalization
Model training with tuned hyperparameters
Validation and testing on unseen data
Visualization using Grad-CAM to highlight affected regions

We achieved accuracy scores of 87%, 89%, and 82% respectively.

What We Learned

The importance of balanced, clean datasets
How preprocessing greatly improves model performance
Interpretability tools like Grad-CAM help build trust in AI
Collaboration between AI and healthcare professionals is essential

Challenges We Faced

Limited availability of labeled medical images
Overfitting due to small sample sizes
Training time and hardware limitations
Ensuring the model is generalizable across diverse patient data

This project gave us hands-on experience in applying AI to real-world medical challenges and strengthened our passion for using technology to improve human health.