With a solid academic background and a keen interest in computer vision, deep learning, and machine learning, I am an enthusiastic and proactive AI specialist. I’m now working on a master’s degree in artificial intelligence, with a focus on creating cutting-edge solutions for hyper-spectral data analysis and medical imaging.
I’ve worked on significant projects along the way, such as the remarkable 99.20% accuracy in lung cancer detection using CNN and LSTM. My experience in medical AI applications, where accuracy and creativity are crucial, has been further refined by my contributions to breast cancer diagnosis and brain tumor segmentation.
Beyond technical accomplishments, I respect teamwork, ingenuity, and problem-solving. In positions like Machine Learning Instructor to Google Developer Student Clubs, I enjoy sharing my skills, mentoring others, and encouraging community involvement.
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Developed U-Net and CNN hybrid, achieving 99% segmentation accuracy.
Achieved 99.20% accuracy using CNN and LSTM models optimized with TPOT.
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