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Meet the Team
Introduction: CAD Systems
- SCI publications: 25+
- Proposed networks: PC-Snet, G-net, DC-Gnet, GC-NET, RCNN-Crop, Colpo-net, Polyp-net, CoG-NET.
- Primary Coding Language: Python
- Other libraries and tools used: Tensorflow, Scikit, Pytorch,
Numpy, Pandas, matplotlib
To design and develop a Computer aided diagnosis (CAD) systems based on convolutions due to its self-learning capability, which can identify patients with abnormalities such as prostate cancer, pancreatic cancer, glaucoma, cervical cancer, colorectal polyps at an early stage in order to ease the work of doctors.
Early and speedy diagnosis and prognosis of cancer and help doctors in making effective treatment plans promptly.
Accelerate the diagnosis process, make diagnosis objective, and reduce any diagnostic divergence
resulting from different observers.
Accurate result without help of specialists.
Low cost and easily accessible diagnosis to help millions of people worldwide.
Support imaging modalities such as MRI, CT, X-ray and fundus etc.
Auto-crop, segment and classify the desired Region of Interest (ROI) using deep convolutional networks.
Semi and fully automated and less error prone diagnostic procedures.
Provide assistance to doctors by reducing their manual diagnostic hours.
- Name of the Department: UIET, Panjab University
- Name of Project: Design Innovation Centre(DIC)
- Name of Group : :Medical Devices and Restorative
Partners: PGIMER , GMCH 32, Chandigarh