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87tana/README.md

Tannaz Mostafid

DevOps & Cloud Computing | Data & MLOps | Computer Vision

👋 About Me

  • Background in MRI image analysis,signal processing and computer vision.
  • Interest in foundation models, RAG, and healthcare AI
  • Hands-on with AI deployment, Docker, cloud, and CI/CD

🧰 Tech & Tools

📊 GitHub Stats

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📚 Publications / Writing

  • From Docs to Dialogue: Build Your RAG Chatbot on Azure, Step-by-Step
  • How I Tried (and Struggled) to Teach YOLO to Spot Bone Fractures
  • Deep Learning for X-ray Bone Fracture Detection: Key Concepts and Recent Approaches
  • Exploring CNN Components for Tumor Segmentation in MRI Images: An Ablation Study
  • Tumor Semantic Segmentation with U-Net and Deeplabv3+
  • Overview of VGG16, ResNet50, Xception and MobileNet Neural Networks
  • Brain Tumor Classification using VGG16, ResNet50, Xception & MobileNet on MRI Images
  • Text Preprocessing & BoW Vectorization for NLP Sentiment Analysis (Amazon Reviews)

🔗 Links

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  1. cloud-devops-llmops-platform cloud-devops-llmops-platform Public

    fully containerized LLM platform running on Azure.

    HCL 3 1

  2. RAG_Chatbot RAG_Chatbot Public

    Developed and deployed a cloud-hosted RAG chatbot with a Streamlit UI to semantically ingest, index, and query diverse text datas, delivering context-rich, on-demand insights while managing everyth…

    Jupyter Notebook

  3. YOLO11n-Bone-Fracture-Detection-Model YOLO11n-Bone-Fracture-Detection-Model Public

    This repository implements an end-to-end pipeline for detecting bone fractures in X-ray images using the YOLO11n object detection framework. All methods, datasets, and insights are drawn from the a…

    Jupyter Notebook 2 1

  4. SemanticSegmentationMedicalImaging SemanticSegmentationMedicalImaging Public

    In this project, I manipulate the impacts of different components of an encoder-decoder convolutional neural network (CNN) for tumor segmentation in the 2D MRI Brain Tumor Image Dataset.

    Jupyter Notebook

  5. ImageObjectClassification_NetworkComparison ImageObjectClassification_NetworkComparison Public

    In this study, the MobileNet, ResNet50, VGG16, and Xception deep learning architectures were trained to classify brain MRI images into four various cancer categories , and a healthy "no tumor" class.

    Jupyter Notebook 2

  6. 3-tier-application 3-tier-application Public

    Forked from Pokfinner/3-tier-application

    C# 1