I'm an AI Engineer specializing in Medical Imaging. My work focuses on deep learning for healthcare applications, particularly GAN-based synthetic medical image generation, classification, and segmentation of X-rays, MRIs, and CT scans.

• 🔭 Current Focus: AI-based Medical Imaging Research
• 🧠 Project: Developing a Synthetic Medical Image Generator (DeepMedSynth) using GANs
• 📊 Interest Areas: Medical Image Processing, Explainable AI (XAI), AI-driven Healthcare Solutions
• 🛠️ Tech Stack: Python, PyTorch, TensorFlow, OpenCV, Numpy, Scikit-Learn

• Developing a Generative Adversarial Network (GAN) to generate synthetic MRI/X-ray images.
• Technologies: PyTorch, TensorFlow, GANs
• GitHub Repository

• Implemented YOLOv8-based Object Detection for real-time applications.
• Applied custom training on specialized datasets for medical and general-purpose image analysis.
• Technologies: PyTorch, OpenCV, Ultralytics YOLOv8
• GitHub Repository

• Implementing Grad-CAM, SHAP, and LIME for explainable deep learning models in healthcare.
• Repo coming soon

• 📊 Data Preprocessing: Cleaning, normalizing, and augmenting medical imaging datasets for deep learning models.
• 🧠 Model Training & Optimization: Implementing and fine-tuning deep learning models (GANs, CNNs, Vision Transformers) for classification and segmentation tasks.
• 🔬 Evaluation & Explainability: Using techniques like Grad-CAM, SHAP, and LIME to interpret AI model decisions in medical imaging.
• 🚀 Deployment & Performance Monitoring: Deploying AI models in healthcare applications and continuously improving performance with real-world feedback.

Below is a snapshot of a typical AI-driven Medical Imaging workflow, showcasing how data progresses from acquisition to AI model predictions:

**Medical Image Acquisition**

**Deep Learning Model Training**

**AI-Powered Diagnosis**

AI-powered medical imaging enhances healthcare by:

1. Improving early disease detection using deep learning on MRI, CT, and X-ray images.
2. Generating synthetic medical images (GANs) to augment datasets and overcome privacy concerns.
3. Enhancing model interpretability through Explainable AI (XAI), ensuring trust in AI-driven diagnoses.

💡 Quote:
"AI in medical imaging is transforming diagnosis, making healthcare faster, more accurate, and accessible."

🌟 Let's revolutionize medical imaging with AI! 🚀

Feel free to explore my other repositories for more insights into my work and contributions.

• 💼 LinkedIn
• 📧 Email

🚀 Let's advance AI in Medical Imaging together!