Inam15 01: Mp4

: Reached 90.17% test accuracy in classifying diabetic retinopathy.

: Uses a Compact Convolutional Transformer (CCT) to reduce computation time while processing low-resolution images.

: A low-complexity, efficient deep learning framework. INAM15 01 mp4

: Mention the use of massive datasets (over 53,000 images) and techniques like DCGAN for data augmentation.

: Use a split-screen layout showing the raw fundus image on one side and the AI's diagnostic heatmap on the other. 3. Case Study or Blog Post : Reached 90

Can you tell me more about the or who your target audience is so I can refine this draft?

If you are using the video for social media (e.g., Instagram Reels or TikTok), you can use typical hooks for medical AI: : Mention the use of massive datasets (over

: "AI is changing the game for eye health! 👁️✨ Watch how our model detects Diabetic Retinopathy in seconds. #AI #MedicalTech #DeepLearning #HealthInnovation"