Retina AI Heart Risk Predictor

@sant527

I work in python mainly. I also work with javascript/reactjs for frontend I have good knowledge of database, cloud deployment and docker containarization of apps. I worked with creating chat bots using chatgpt using rag pipeline I would like to work on this project. Lets discuss more on this project

@mike3608

With extensive experience in developing deep-learning models, particularly CNNs, and deploying AI solutions via web interfaces, I am confident in delivering an end-to-end prototype that meets your specifications. My expertise includes Python, TensorFlow, and React, ensuring seamless model training and user-friendly front-end design. I am keen on collaborating closely to refine the model and interface, ensuring superior performance and readability in the final product. Looking forward to devising a robust tool that accurately assesses heart-attack risk from retina images.

@niveditaj1

Hello, I have reviewed your project to develop a deep-learning pipeline for heart attack risk prediction from retinal images. This is a fascinating application of computer vision in healthcare, and it aligns perfectly with my experience in building end-to-end deep learning solutions. I can develop the complete system, from the CNN model and server-side processing with Python to the user-friendly web interface with the required heat-map visualization. QUERIES 1) Regarding the dataset, do you have a specific annotated dataset in mind, or will the initial phase of the project involve identifying and possibly labeling a suitable one? 2) You mentioned experimenting with different architectures. For the initial prototype, should I focus on a proven baseline model first, or allocate time for experimenting with several architectures? 3) For the heat-map visualization, are there specific explainability techniques (like Grad-CAM) you prefer, or should I implement the most suitable one based on the final model architecture? 4) Do you want me to deliver you the code or I have to set it up on your system too? Thanks, Nivedita

@MudassirMadah453

Hello Prajyot, I am Mudassir Ahmad, emphasizing JavaScript, Python, and HTML5 with 1 year of experience. I've thoroughly reviewed your project requirements and am excited about the Retina AI Heart Risk Predictor challenge. My approach involves leveraging my expertise in deep learning, Python, TensorFlow, and OpenCV to develop a robust model for heart risk prediction based on retina-fundus images. I will ensure a seamless web interface where users can upload images for real-time risk assessment. With a focus on accuracy and interpretability, I will provide well-commented code, model weights, and a user-friendly UI for smooth deployment. Given my experience in web automation, API integration, and custom solutions, I guarantee a tailored, efficient, and scalable approach. You can view relevant portfolios at: [WordPress WooCommerce Store](https://www.freelancer.com/portfolio-items/10601575-wordpress-woocommerce-store), [WordPress Website Development](https://www.freelancer.com/portfolio-items...

@IsaacSmith77

Hi, I can easily DO your work IN 24 HOURS, DM me now to get started, PRICE NEGOTIABLE 100% Work satisfaction is provided!

@Umair1122003

As a Full-stack Developer and AI enthusiast, I believe I am uniquely positioned to bring your Retina AI Heart Risk Predictor to life. My expertise lies in leveraging machine learning and artificial intelligence for developing intelligent interfaces that deliver seamless user experiences—similar to what you're looking for in your project. Drawing on my solid background in HTML5, JavaScript, Python, and ML, I am confident in my ability to create the sophisticated web-based system you need. From image processing with OpenCV to applying cutting-edge deep learning architectures (such as CNN or RetinaNet) and deploying them on the cloud within frameworks like Streamlit/FastAPI and React—I have a robust toolkit up my sleeve. Moreover, I champion clean, well-commented code alongside thorough documentation. I promise excellent accuracy/performance and a detailed README covering environment setup, dataset preparation, and instructions for retraining/fine-tuning. Let's work together to develop an amazing tool that outperforms baselines and delivers sensible ROC/AUC scores—providing clear prediction and risk assessments for the effective management of heart health.

@Vikibiradar

Hello Dear I’m Vikram Biradar, I believe I am the right candidate for this proposal because Previously, I developed a full Alzheimer’s Disease Detection system using MRI scans, which involved advanced CNN architectures, detailed evaluation metrics, and deployment via Streamlit. This experience equips me to handle your project with the same level of technical knowledge and reproducibility. I am AI & Data Science developer experienced in building end-to-end deep learning pipelines and deploying them as interactive web applications. For your requirement, I can develop a CNN-based system for heart-attack risk prediction from retina-fundus images, experimenting with architectures such as RetinaNet and ResNet to achieve optimal performance. The pipeline will include preprocessing with OpenCV, model training in TensorFlow/PyTorch, and explainability through attention/heatmap overlays to highlight key retinal regions influencing predictions. The final solution will be delivered as a web-based prototype using FastAPI with React, enabling users to upload scans and receive real-time risk scores (Low/Medium/High) along with confidence percentages. We can connect for further discussion.

@EvyolIT

I'm excited by the opportunity to build your end-to-end Retina-based heart risk predictor. With experience in CNNs, medical imaging, and full-stack AI deployment, I can deliver a working prototype that ingests fundus images, processes them through a fine-tuned deep learning model (e.g., ResNet, EfficientNet, or custom RetinaNet), and outputs risk scores with confidence levels and a visual heatmap. I will use TensorFlow or PyTorch with Grad-CAM or similar attention methods, and deploy via a clean Streamlit or FastAPI + React UI that runs seamlessly on a cloud VM. You’ll receive all source code, trained weights, and documentation for easy retraining and deployment. I can begin immediately and aim to deliver a functional first version within 15 days.

@youssefDS3

HI, I’m confident I can deliver your project successfully. I have strong hands-on experience with TensorFlow and PyTorch for image classification tasks, including CNNs and Vision Transformers, and I’ve applied transfer learning in real-world projects to reach production-ready performance. On the web development side, I can design a clean and responsive UI with React and integrate it seamlessly with a FastAPI backend for efficient model serving. Here’s what you can expect from my work: 1- Modern, polished UI with smooth animations and intuitive drag-and-drop image upload. 2- Reliable backend powered by FastAPI for efficient and scalable inference. 3- High model accuracy: I’ll benchmark multiple architectures (CNNs, EfficientNet, ViT, etc.) to find the best-performing setup. 4- Comprehensive documentation: A well-structured README explaining model architectures, hyperparameters tested, preprocessing/augmentation strategies, and setup instructions. 5- Full support until deployment: I’ll assist you through testing and cloud deployment so the app runs smoothly end-to-end.

@punyabr

I have hands-on experience developing healthcare-focused AI systems, including a published research project where I built a hybrid CNN + Transformer model to detect Parkinson’s disease from medical drawings. I’ve also worked on heart disease prediction using decision trees and random forests, emphasizing both accuracy and interpretability through feature importance and model insights. These projects give me a solid foundation in designing medical AI solutions that balance predictive performance with transparent explanations. For your retina-fundus prototype, I can develop and benchmark CNN-based architectures (e.g., RetinaNet, EfficientNet) in TensorFlow or PyTorch, ensuring strong generalization beyond naive baselines. To enhance trust and usability, I can add interpretability features such as Grad-CAM heatmaps or attention overlays that clearly highlight retinal regions influencing predictions. The final solution will run end-to-end in a streamlined web interface (Streamlit/FastAPI + lightweight front end), with clean, well-documented code, model weights, reproducible environment setup, and a README for dataset preparation and retraining. Having already built similar healthcare AI applications, I can deliver a reliable system that not only predicts heart-attack risk scores from retinal scans but also explains its reasoning, an essential step for clinical adoption.

@carolyncc20

Hi, I am an AI/ML developer with experience in medical image analysis, including eye cancer detection using CNN models. I can develop a retina AI heart risk detector, delivering a working prototype with clean, documented code. I can also handle preprocessing and optimization to ensure accurate results. I am excited to discuss your requirements and start quickly.