The problem DIAGNO PLUS + solves

World's doctor-to-population ratio is very low. The workload on doctors is intensely immense, hence decreasing their own average life expectancy. Diagnostic error is one of the most important safety problems in health care today and inflicts the most harm. Major diagnostic errors are found in 10% to 20% of autopsies, suggesting that 40,000 to 80,000 patients die annually in the U.S. alone from diagnostic errors. Diagnostic problems include:
• Wrong labeling of samples
• Clerical mistakes
• Improper performance of the tests
• Instruments failure
• Quality of the poor reagents
• Mistakes in the calculation of the result
• The delayed performance of the tests
• Expensive diagnostics tests
• Whole process is time-consuming and requires traveling costs
• These services are not available for people living in remote areas
• Creates a lot of biomedical wastes
All these errors can be resolved through automation. Also, diagnostics errors are very common nowadays, which can’t be ignored. Therefore, our idea is to develop a comprehensive application for disease diagnosis using the thriving technology of artificial intelligence. Concepts like CNN, K-means clustering, decision trees, VGG-16, linear regression and other AI techniques will constitute the foundation of our disease diagnostic algorithms
This approach is cost effective as it saves all the transportation charges and all the money that a patient spends on different types of lab tests. And you ever thought how much time and pain does it take for different lab tests required to diagnose, hence prolonging your diagnose and treatment. This code can make your diagnosis much faster and painless. This code would keep in environment friendly diagnosis as it will reduce the carbon footprints by keeping all the data in digital format and removing the need of physical copy of the X-rays and all the scanned data is stored digitally which is easier to transfer.

Challenges we ran into

CNN model training
image uploading while prediction
handling CSRF token validation