You will get AI-Powered Diabetic Retinopathy Detection
Project details
Detecting diabetic retinopathy early can save lives and prevent vision loss. With my expertise in biomedical engineering and AI, I provide a reliable, high-performance solution for detecting diabetic retinopathy from retinal images. Using state-of-the-art deep learning models, I ensure accuracy and efficiency in diagnosis, backed by thorough validation and clear reporting. Whether you need a basic detection system, advanced grading, or a fully deployed web app, I am committed to delivering results tailored to your needs.
Machine Learning Tools
Keras, MLflow, NumPy, OpenCV, pandas, Python, PyTorch, scikit-learn, SciPyWhat's included
| Service Tiers |
Starter
$150
|
Standard
$250
|
Advanced
$400
|
|---|---|---|---|
| Delivery Time | 7 days | 21 days | 50 days |
Number of Revisions | 3 | 5 | 5 |
Number of Model Variations | 1 | 3 | 4 |
Number of Scenarios | 2 | 4 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$25 - $1,000
Additional Revision
+$20
Additional Model Variation
(+ 5 Days)
+$30About Ghulam
AI/ML Engineer | Medical Imaging | Healthcare LLMs | Clinical Trials
Karachi, Pakistan - 5:36 pm local time
With 5+ years at the intersection of medicine and machine learning, I specialize in high-stakes AI where precision is a clinical requirement. My work is published in Neural Networks (Elsevier, 2025) and validated through two registered clinical trials (NCT05883072, NCT06287645) focused on LLM integration in patient care.
🔬 Specialized Expertise
Computer Vision & Retinal Imaging — State-of-the-art diabetic retinopathy classification and enhancement using RETFound, EfficientNet, and custom U-Net architectures. Evaluated against clinical ground truth with AUROC, sensitivity, specificity, and Kappa metrics.
Clinical LLM & RAG Pipelines — Safe, context-aware LLM applications with robust safety evaluation frameworks for healthcare providers. I don't just integrate APIs — I build guardrails, evaluate failure modes, and align outputs with clinical standards.
Clinical AI Applications — End-to-end development of AI-powered mobile health apps (Android/Kotlin + FastAPI), including bilingual LLM chatbots with domain-specific safety guardrails, deployed and validated in real patient contexts.
End-to-End MLOps — Scalable pipelines on AWS (SageMaker/EC2) with Docker, ensuring models move from research to clinic without losing rigor.
🌐 Research & Collaboration
My methodologies are shaped by active collaborations with University College London (UCL) and Tongji University Shanghai, focusing on the next generation of medical foundation models.
🛠️ Technical Stack
Frameworks: PyTorch, TensorFlow, MONAI, HuggingFace, OpenCV
Deployment: FastAPI, Android/Kotlin, Docker, AWS (S3, SageMaker), Streamlit
LLM Integration: Claude API, ChatGPT API, RAG pipelines
Protocols: Clinical grading (ETDRS, ICDR), medical dataset annotation
How We Can Work Together
Whether you need a custom diagnostic model, an LLM safety evaluation for a clinical setting, or an end-to-end medical AI application — from research architecture to production deployment — I bring both the scientific rigor and the shipping experience your project demands.
If you need an AI system that can hold up to clinical scrutiny — validated metrics, safety evaluation, and a clear path to deployment — let's talk.
📩 Available for new projects. I typically respond within 24 hours.
Steps for completing your project
After purchasing the project, send requirements so Ghulam can start the project.
Delivery time starts when Ghulam receives requirements from you.
Ghulam works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering:
I will work with you to understand your needs, such as datasets, performance targets, and deployment preferences.
Data Preprocessing and Analysis
Prepare the dataset (if provided) for AI model training, including cleaning, augmentation, and normalization.