DiagKnow

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  App Splash Screen

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  App Eye Test Demo

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  DiagKnow User Dashboard

DiagKnow was inspired by the urgent need to address Bangladesh’s critical healthcare challenges, particularly the lack of access to affordable, timely, and accurate diagnosis, especially in rural and underserved communities. With misdiagnoses, delayed treatments, and expensive tests being common issues, I set out to build an inclusive digital solution that bridges the diagnostic gap.

DiagKnow is an AI-powered mobile app that enables users to detect diseases through simple image or audio inputs. From identifying eye and skin conditions to detecting heart abnormalities and hearing loss, the app uses advanced machine learning models to provide fast, reliable results.

DiagKnow uses deep learning models trained on medical images and sounds, integrating them into an intuitive Android application.

Throughout development, I faced challenges such as sourcing quality medical datasets, training models for high accuracy across diverse inputs, and ensuring the app remains lightweight and user-friendly for lower-end devices.

I am proud of successfully integrating multiple disease detection models in one unified platform and making diagnosis possible through just a smartphone. Most importantly, I built a product that has the potential to improve heatlh services in low-resource settings.

In the process, I learned the value of balancing technical accuracy with user accessibility and how crucial localized solutions are in healthcare innovation.

Next, DiagKnow aims to expand its disease coverage, support regional languages. Finally, Launch the app for beta testing.

Built With

• android-studio
• bolt.dev
• cnn
• java
• tensorflow
• yolo11