Skip to content
View Khalid-Siddiqi's full-sized avatar
🎯
Focusing
🎯
Focusing
8 followers · 15 following

Achievements

Achievement: Pull Sharkx2Achievement: YOLO

Achievements

Achievement: Pull Sharkx2Achievement: YOLO

Highlights

  • Pro

Block or report Khalid-Siddiqi

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
Khalid-Siddiqi/README.md

👋 Hi, I’m @Khalid-Siddiqi

🎓 Computer Science Graduate from FAST NUCES Karachi
🧠 Passionate about Machine Learning, Deep Learning & Computer Vision
💼 Currently leading research on an AI-based Diabetic Patient Management (ADPM) System
🎯 Seeking opportunities to participate in national and international Computer Vision & AI competitions to apply and hone my skills.

🏆 Achievements

  • 🥉 3rd place in Procom Computer Vision Competition 2025
  • 🥉 3rd place in PaysysLab AI Competition 2025

🔬 Research & Projects

  • Developed an Image-Based Recommender System for Diabetic Foot Ulcer (DFU) treatment using:
    • ConvNeXt, Swin Transformer, Vision Transformer (ViT), and 6+ CNN architectures
    • Hybrid approach: Deep learning for wound classification + XGBoost for treatment recommendation
  • Implemented YOLOv11 for real-time instance segmentation in medical images
  • Built full-stack web and mobile app tools for image annotation and diabetic ulcer analysis using FastAPI, TensorFlow.js, and REST APIs. The system generated downloadable PDF reports including segmented overlays, treatment recommendations, and personalised care plans.

🧰 Tech Stack

  • Languages: Python (FastAPI), Node.js, C/C++
  • Frameworks: PyTorch, TensorFlow, OpenCV, Scikit-learn
  • Databases: MySQL, MongoDB, Supabase
  • Deep Learning Models: Swin Transformer, ViT, ConvNeXt, YOLOv11, ResNet, Inception, EfficientNet, DenseNet, etc.

🎯 Goals

  • Compete in national & international speed coding and AI/Computer Vision competitions
  • Apply ML/DL to real-world healthcare challenges

🔗 Portfolio & Contact

Pinned Loading

  1. Swin-ResNet-Ensemble-for-Custom-DFU-Grading Swin-ResNet-Ensemble-for-Custom-DFU-Grading Public

    This project implements an ensemble of Swin Transformer and ResNet models for grading diabetic foot ulcers (DFU) into four severity levels. By leveraging the complementary strengths of CNNs and Tra…

    Python 1

  2. DFU-Detection-Segmentation-with-YOLOv11-Full-Stack-Mobile-Deployment DFU-Detection-Segmentation-with-YOLOv11-Full-Stack-Mobile-Deployment Public

    This project includes the implementation and code for annotating Diabetic Foot Ulcer (DFU) images using the YOLOv11 instance segmentation model.

    Jupyter Notebook

  3. DFU-vs-Noise-Classification DFU-vs-Noise-Classification Public

    Jupyter Notebook

  4. Image-Based-Recommender-System-for-Personalized-Diabetic-Foot-Ulcer-Treatment-Using-Deep-Learning Image-Based-Recommender-System-for-Personalized-Diabetic-Foot-Ulcer-Treatment-Using-Deep-Learning Public

    An end-to-end system for automated Diabetic Foot Ulcer (DFU) grade classification, treatment recommendation, explainability, and report generation — integrated with a mobile app and powered by deep…

    Python