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Hi there! 👋 I'm Sai Rahul Kodeboina

I'm a B.Tech CSE student specializing in AIML, currently in my 3rd year at Vellore Institute of Technology, Andhra Pradesh.

🔍 About Me

  • 🎓 Studying B.Tech CSE with Specialization in AIML
  • 🌐 Interested in Deep Learning, Data Science, and Web Development
  • 📍 Located in Vijayawada, India.

🚀 Skills

  • Programming Languages: Java, Python, JavaScript, PhP, MySql
  • Technologies: TensorFlow, PyTorch, HTML, CSS, ReactJs
  • Tools: Git, Jupyter Notebooks
  • Frameworks: Flask

🌱 Projects

  • Automated Abnormal ECG Detection for Early Heart Disease Diagnosis:(link-to-project1):Developed an advanced LSTM-based model for automated ECG analysis, successfully detecting arrhythmias and other heart conditions with high precision and accuracy. Conducted extensive research in various healthcare topics, contributing to the development of innovative solutions and demonstrating a strong commitment to improving medical diagnostics and patient outcomes.
  • Local Home Services Platform(link-to-project2): Developed a dynamic local home services platform from scratch using PHP, MySQL, HTML, CSS, and JavaScript. Acquired proficient skills in backend scripting, database management, and frontend design, resulting in a fully functional and user-friendly website.
  • Lung Cancer Classification using Chest CT-Scan images(link-to-project3): Developed a VGG19 model for precise lung cancer classification using CT scan images. Leveraged transfer learning by initializing the model with pre- trained weights from ImageNet. Achieved over 98% accuracy in identifying various lung cancer types, including adenocarcinoma, large cell carcinoma, normal tissue, and squamous cell carcinoma.

📫 Contact Me

Feel free to explore my repositories and connect with me! 😊

Sai Rahul Kodeboina's Projects

deeplearningproject icon deeplearningproject

Developed an intelligent system for automated ECG analysis, providing detailed reports on arrhythmia classification, ST segment and QT interval analysis, treatment response evaluation, and cardiac health status assessment, contributing to improved cardiac diagnostics and patient care.

lungcancerclassification icon lungcancerclassification

Developed a deep learning model that can accurately classify chest CT-Scan images into different categories representing lung cancer types (adenocarcinoma, large cell carcinoma, squamous cell carcinoma). Early and accurate detection of lung cancer can significantly improve patient outcomes and guide appropriate treatment strategies.

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