CLEAR VISION

Techiee Hackers

Inspiration

The inspiration behind Clear Vision stems from a deep-rooted commitment to addressing critical challenges in healthcare. Diabetic retinopathy, a progressive eye disease resulting from diabetes complications, poses a severe threat to vision, potentially leading to blindness if untreated. Symptoms include blurred vision, floaters, and difficulty seeing at night. Regular eye exams and early detection in managing diabetic retinopathy is highly important , whereas the limitations of manual diagnosis systems became evident, marked by subjectivity, limited availability, and time-consuming processes. Acknowledging these shortcomings, particularly in the context of diabetic retinopathy, Clear Vision embarks on a mission to harness the power of artificial intelligence.

What it does

Our AI-based solution for retinopathy diagnosis offers a web application that utilizes image processing and machine learning. With high diagnostic accuracy, AI predictions, and streamlined processes, our solution overcomes the limitations of the existing system. Our AI-based medical imaging system overcomes challenges in Diabetes Retinopathy diagnosis

Objective Results: AI algorithms provide standardized assessments, reducing subjectivity and ensuring accurate diagnoses.

Enhanced Accessibility: The system is accessible remotely, improving access to expert-level diagnoses, especially in underserved areas.

Streamlined Processes: AI automates analysis, reducing diagnosis time and enabling timely interventions.

Automated Reporting: AI generates comprehensive reports.

How we built it

Building an Effective Diabetic Retinopathy Prediction Model using ResNet Input Layer: The input layer expects images of size 224x224 pixels with 3 channels (RGB).

ResNet50 Base Model: The base model is ResNet50, a deep convolutional neural network with 50 layers. It has been pre-trained on the large-scale ImageNet dataset, which enables it to extract meaningful features from images.

Global Average Pooling Layer: The output of the base model is passed through a global average pooling layer. This layer reduces the spatial dimensions of the features to a fixed size, resulting in a 1D feature vector for each image.

Dense Layer: A dense layer with 256 units and ReLU activation is added on top of the global average pooling layer. This layer introduces non-linearity and enables the model to learn more complex representations.

Final Dense Layer: The last layer is a dense layer with the number of units equal to the number of classes (5 in this case). It uses the softmax activation function, which produces class probabilities for each input image. The model predicts the class with the highest probability as the final prediction. Building a responsive system using Streamlit Developed an AI model to analyze eye scans for diabetic retinopathy

Categories severity into five levels: no DR, mild, moderate, severe, and proliferative

Provides summary reports (DB Visualisation) of patient retinal image scans in the database

Challenges we ran into

On the Clear Vision journey, we navigated challenges in curating a diverse dataset, achieving model interpretability, and ensuring secure remote accessibility testing our precision and adaptability. Designing an intuitive user interface demanded multiple iterations. Gaining clinical validation and collaboration underscored the need for trust-building efforts. Fine-tuning the ResNet50 model, addressing scalability concerns, and navigating ethical considerations showcased our commitment to excellence. Each challenge, from data intricacies to ethical responsibilities, became a catalyst for growth, propelling Clear Vision toward its goal of redefining diabetic retinopathy diagnosis with impact and efficacy.

Accomplishments that we're proud of

Clear Vision stands tall with a series of achievements as our AI algorithms have achieved exceptional diagnostic accuracy, setting new standards in diabetic retinopathy diagnosis and providing healthcare professionals with objective, standardized assessments. The web application's remote accessibility feature has democratically extended expert-level diagnoses, breaking geographical barriers. By automating analysis, generating comprehensive reports, and streamlining processes, Clear Vision ensures timely interventions and empowers healthcare professionals. The model architecture, incorporating ResNet50 and advanced algorithms, stands as a testament to efficiency and robustness. User-centric interface design and database visualization features have earned acclaim for their intuitiveness. All these represent Clear Vision's commitment to redefining diabetic retinopathy diagnosis with excellence and compassion.

What we learned

The development of Clear Vision has been a profound learning journey, molding our approach to healthcare innovation. We've embraced adaptability, acknowledging the dynamic nature of healthcare and technology. Fundamental to our evolution is a steadfast commitment to user-centric design, emphasizing simplicity and functionality. Collaborating with healthcare providers has emphasized the significance of real-world validation and continuous learning in AI integration. These principles stand as our guiding lights, propelling Clear Vision towards reshaping diabetic retinopathy diagnosis with innovation, compassion, and efficacy

What's next for CLEAR VISION

Clear Vision is set to make a big leap by introducing AI-driven decision support features. In simpler terms, it means our system will not only diagnose but also offer helpful suggestions to healthcare professionals in planning treatments by a smart assistant that not only spots issues but also provides insights to make decisions easier. Clear Vision's next step is all about going beyond diagnosis – it's about being a helpful sidekick for healthcare professionals, making their job smoother and improving patient care. The future of Clear Vision is not just seeing better; it's about making healthcare decisions clearer and more efficient.

Conclusion - Clear Vision's Impact on Healthcare:

Clear Vision aims not just to be a technological innovation but a transformative force in diabetic retinopathy diagnosis. Through continuous improvement, collaboration, and community engagement, we envision Clear Vision making a substantial positive impact on patient care and contributing to advancements in the field of medical AI.

Built With

• deep-learning
• machine-learning
• python
• streamlit