AI Diabetic Retinopathy Grading

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@nrajuu07

I'll build a research-grade DR grading pipeline across OLIVES, MMRDR, and your curated set — multimodal inputs, domain adaptation, self-supervised pre-training, per-patient progression tracking, and clinically interpretable Grad-CAM/attention heatmaps per prediction. Deliverables: clean reproducible code, trained weights with batch inference script, full experiment report, and a walkthrough for end-to-end reproduction — targeting SOTA F1 with under 5% cross-dataset drop.

@HanyasSalem

Hi there, As a highly skilled statistician and data scientist who understands the nuances of handling medical data, I am confident in my ability to tackle your project's complex tasks. With a background in both medicine and data analysis, I can competently wrangle and interpret your vast collection of fundus photographs and patient information. My experience with Python will enable me to conduct your required model with precision and attention. Moreover, as a physician myself, I'm well-versed in translating raw data into actionable insights and can generate visualizations that are not just technically sound but also clinically meaningful. I understand the critical importance of providing comprehensive reports that meet rigorous scientific standards while remaining approachable.

@amirrmusavifr

Hi, I'm an experienced Python developer with the necessary skills to complete your project. I have skill sets for tasks: • Data Preprocessing: Handle missing values, normalize data, and encode categorical variables. • Feature Engineering: Generate meaningful features like lagged sales, holiday flags, or time-based trends. • Model Selection and Justification: Propose and implement a suitable model (e.g., Regression, Random Forest, Gradient Boosting) and justify its use. • Evaluation and Insights: Evaluate the model with metrics such as MAE, MSE, and RMSE, and provide actionable business recommendations based on the predictions. I have done projects on data using Pandas, NumPy, and SciPy. I’m able to interpret data and provide actionable insights. Also, I have Deep understanding and experience in data analysis with Python. My track record of success with similar projects is proof that I can deliver results quickly and accurately. If you're interested in hearing more about how I could help you, please don't hesitate to reach out! I can provide the requirements with minimum time and cost.

@innovateNsolve

Freelancer proposal Hello, I am a researcher and trainer in computer vision, machine and deep learning having PhD in Computer Science with 25+ years of experience. I have worked on several research projects in these domains with few research publications as well. I have strong theoretical knowledge with hands-on experience in AI/ML, Computer Vision, python, OpenCV, Tensorflow, MATLAB etc. I have gone through the scope of the project. I can work on this research-grade, modular, GPU-optimized multimodal retinal AI framework for Diabetic Retinopathy (DR) grading, Diabetic Macular Edema (DME) detection, Retinal biomarker localization, Temporal disease progression modeling, Cross-dataset generalization, Explainable retinal AI assuming GPU infrastructure is provided by you. Hope to have further discussions in this regard. Thanks.

@hassam945

Hi,I have already worked on a project named AI System to Detect Diabetic Retinopathy , which you can see in my freelancer profile portfolio: https://www.freelancer.pk/u/hassam945. I understand your requirement: you want a research-grade, reproducible Diabetic Retinopathy pipeline that goes beyond simple classification focusing on accurate grading, cross-dataset generalization, explainability, and patient-level progression tracking with clear clinical interpretability. For your system, I would design a research-grade pipeline: • Multi-dataset ingestion (OLIVES, MMRDR, custom data) with normalization and augmentation • Swin Transformer-based DR grading with attention refinement • Explainability using Grad-CAM / attention heatmaps for clinical trust • Domain adaptation + cross-dataset validation to minimize performance drop • Longitudinal patient tracking for progression trends • Reproducible inference pipeline with DR grade + early warning outputs I can deliver clean, well-documented code, trained weights, evaluation reports, and inference scripts optimized for single GPU. Regarding budget, we should first align on scope and then define a proper budget so quality is not compromised. Looking forward to your response.

@sudhir0112

I specialize in deep learning for diabetic retinopathy grading. MIT graduate with Computer Vision and ML expertise, having built classification models for medical imaging at Axtria pharma projects. I have experience with CNN architectures (ResNet, EfficientNet, VGG), fundus image preprocessing, and DR grading (0-4 scale). Can deliver high-accuracy models.

@toqeer74

The challenge of achieving robust Diabetic Retinopathy grading while ensuring explainability is pivotal for clinical acceptance. With thousands of color fundus photographs at your disposal, a tailored approach using a framework like PyTorch can empower the model to not only classify severity levels but also surface early-stage indicators effectively. To enhance interpretability, class-activation maps and attention heatmaps will be integrated, providing actionable insights for clinicians. The initial deliverable, including well-documented code and comprehensive explanations, will be ready within 30 days. What does success look like for you at the end of this project?

@bishtchirag13

Hi shikha is that you, I specialize in medical computer vision and PyTorch. I can build this educational pipeline for the OLIVES dataset, focusing heavily on the rigorous explainability and evaluation metrics required for your academic presentation. Given the project scope, here is my exact approach to deliver clean, presentation-ready code: 1. Multimodal Architecture: I will build a PyTorch pipeline that fuses fundus images (using a lightweight pre-trained CNN like ResNet50 for image feature extraction) with the tabular clinical metadata (via a dense network). I will handle class imbalance using weighted loss functions to ensure highly reliable F1 and Cohen Kappa scores. 2. Explainability & Visuals (Your Core Focus): In medical ML, interpretability is everything. I will implement: Grad-CAM Heatmaps: Overlaid directly on the retinal images to prove the model is looking at actual lesions/biomarkers, not background noise. Performance Plots: ROC, PR curves, Calibration plots, and Confusion Matrices. Note: Since Dice and IoU are typically segmentation metrics, I will apply them specifically to the biomarker masks provided in the OLIVES dataset. 3. Deliverables: You will receive a perfectly documented Jupyter Notebook (runnable end-to-end), saved .pth weights, the mapped folder of visual outputs, and the 2-page clinical interpretability report. I am ready to start right now. Let's get this built! Best regards, Chirag Bisht