Chakshu_Rakshak

Using Fundoscopic Images of the Retina, ‘Chakshu_Rakshak’ determines whether or not a person has Cataract.

Cataract detection could become faster, more accurate, and more accessible as a result of this.

This will be especially useful in locations where ophthalmologists are few, as persons even with only a high school diploma can be educated to take fundoscopic images.

This would serve another purpose of generating employment in the less educated sections.

This Artificial Intelligence Model may then scan the fundoscopic images provided by a fundus camera and make a prediction.

This would dramatically minimise the amount of time required for an eye examination.

It will also address the issue of lack of availability of ophthalmologists.

The salary of an ophthalmologist ranges from 1.5 lakh INR to 7.5 lakh INR. Hence, this AI system would be an economical alternative in regions of less accessibility.

Using our Computer Vision Artificial Intelligence Model named ‘Chakshu_Rakshak’ can make Cataract detection faster, more accurate, and more accessible.

Honestly speaking, our experience of coding was limited. But the workshops attended today has widened the horizon. We could incorporate all the tools that we learnt today (except Scratch). Apart from that we incorporated AI and ML (Computer Vision). We even tried to create a UI using StreamLit but due to time constraint we couldn't add that. So, we created a simple UI using HTML

The first and foremost challenge was training the computer just like a baby. Initially, its accuracy wasn't good and we got frustrated. But somehow, using various combinations we created an optimum model

Next was creating it User Friendly. We tried to use StreamLit but since we were new to it, we dropped the plan after multiple trials. So, at the end we are up with a simple HTML UI