| Idea Name: |
Image classification |
| Slogan: |
detecting disease digitally |
| Supervisor Name: |
Dr Shibli Nisar |
| Supervisor Designation: |
Associate professor |
| Supervisor School: |
MCS |
| Supervisor Department: |
Electrical Engineering |
| Contact number: |
03005853845 |
| Email ID: |
[email protected] |
| Abstract: |
| The 14 disease detection model will classify x ray images of infected patients to thier respective disease. The dataset will be collected from hospitals for training. The best image classification algorithm will be chosen for it. Top models will be tested for best optimum efficiency. The model will classify the scanned x ray into the 14 diseases. The project is a breakthrough in the radiology. |
| What is the unmet need in society that your idea will fulfill ? |
| due to instant rise in diseases doctors found it difficult to tackle it. so our project will bring more ease to doctors. which
will overall save time and help in accuracy. |
| Who needs it ? How many would benefit ? |
| doctors needs it to treat more patients in less time. and all 14 diseases patients can take the benifit out of it. |
| How will the solution works |
| The dataset of 14 diseases i.eHernia ,Infiltration,Atelectasis,Cardiomegaly, Consolidation,Edema,Ef‐ fusion,FibrosMass,Nodule, Pleural , Thickening, Pneumonia, Pneumothorax will be gathered which will be the xray images of infected patients. Different image classification models will be applied to get the best and optimum model. The model will be trained to achive maximum efficiency on the dataset. The trained model will be placed in an web app for online inference and readily results. |
| Who are your competitors ? How is your solution different |
| radiologist are our competitors. and we make it digital . |
| Status: |
new |
| Entry Date & Time: |
2022-12-15 (1838) |
