You will get Multi-Label Ocular Disease Detection System (8 Diseases)
Project details
I will develop a powerful and clinically useful multi-label AI system that can detect up to 8 ocular diseases simultaneously from a single fundus image. This includes Glaucoma, Age-related Macular Degeneration (AMD), Cataract, Diabetic Retinopathy, and other common eye conditions.
Unlike traditional single-disease models, this system is designed to handle real-world cases where patients often have multiple co-existing conditions. It uses a strong ConvNeXt backbone, Focal Loss to effectively manage severe class imbalance, advanced data augmentation (Albumentations), and dynamic per-class thresholds to achieve high sensitivity even for rare diseases.
You will receive a complete solution with Grad-CAM explainability heatmaps (so doctors can visually understand the AI’s decision) and a professional, easy-to-use interactive Gradio web application deployed on Hugging Face Spaces.
This project is ideal for eye hospitals, telemedicine platforms, screening programs, and research institutions looking for reliable, explainable, and production-ready AI for ocular disease detection.
Unlike traditional single-disease models, this system is designed to handle real-world cases where patients often have multiple co-existing conditions. It uses a strong ConvNeXt backbone, Focal Loss to effectively manage severe class imbalance, advanced data augmentation (Albumentations), and dynamic per-class thresholds to achieve high sensitivity even for rare diseases.
You will receive a complete solution with Grad-CAM explainability heatmaps (so doctors can visually understand the AI’s decision) and a professional, easy-to-use interactive Gradio web application deployed on Hugging Face Spaces.
This project is ideal for eye hospitals, telemedicine platforms, screening programs, and research institutions looking for reliable, explainable, and production-ready AI for ocular disease detection.
Machine Learning Tools
ChatGPT, Keras, NLTK, NumPy, OpenCV, pandas, Python, PyTorch, SciPy, TensorFlow, Word2vecWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 4 | 6 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 2 | 3 | 4 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code |
About Huzaifa
AI/ML Engineer | RAG, Medical Computer Vision & LLM Fine-Tuning
Mardan, Pakistan - 11:02 am local time
Notable Projects:
Developed a Medical RAG Chatbot using LangChain, vector databases, and prompt engineering to provide accurate, cited answers while minimizing hallucinations.
Built a Diabetic Retinopathy Detection System (0-4 severity) with PyTorch, Knowledge Distillation, Ensemble methods, and EfficientNet-B0 optimized for edge deployment + Grad-CAM explainability.
Created a Multi-Label Ocular Disease Detection System for 8 concurrent eye conditions using ConvNeXt, Focal Loss, and Albumentations.
Fine-tuned Qwen2.5-Coder-7B via Unsloth LoRA into a capable coding agent and deployed it quantized on Hugging Face Spaces.
Technical Expertise:
Python, PyTorch, TensorFlow/Keras, OpenCV
RAG pipelines, LangChain, Vector Databases, Prompt Engineering
Computer Vision (CNNs, ConvNeXt, Medical Imaging)
LLM Fine-Tuning (LoRA, Unsloth), Quantization (GGUF, ONNX), Gradio deployments
I’m passionate about explainable AI and turning complex models into user-friendly applications. Available for RAG/LLM projects, Medical Imaging AI, Custom Model Development, and AI Agent work.
Let’s discuss how I can help bring your AI project to life with high-quality, reliable solutions.
Steps for completing your project
After purchasing the project, send requirements so Huzaifa can start the project.
Delivery time starts when Huzaifa receives requirements from you.
Huzaifa works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Requirements Gathering
We discuss your specific needs, target diseases, dataset details, and clinical priorities. I analyze sample images to understand data quality and challenges.
Data Preprocessing & Augmentation
Thorough cleaning, resizing, and advanced augmentation using Albumentations to improve model robustness and handle variations in image quality and lighting.