Brain Tumor Detection using CNN

Deep Learning Research Project – Semester Exchange in BK BIET in India - Brain Tumor Detection: • Conducted an independent research mission on deep learning applications in medical imaging. • Architected a CNN-based brain tumor detection model using ResNet50, achieving 92% accuracy through advanced data preprocessing and statistical analysis. • Developed the model in Python with PyTorch, NumPy, Pandas, and Scikit-learn; implemented image classification pipelines in Jupyter Notebook/Anaconda and maintained code via GitHub. • Explored and compared multiple machine learning techniques to enhance performance and robustness. Key Outcomes & Skills Gained: • Acquired strong Computer Vision and Deep Learning expertise, with in-depth comprehension of CNN architectures. • Learned how to work in a research environment following proper research methodology. • Strengthened statistical analysis, data preprocessing, visualization, and data structures expertise. • Gained a deep understanding of model evaluation metrics (accuracy, precision, recall, F1, etc.) and how to apply them effectively and wisely to assess and improve models. • Enhanced ability to communicate fluently in English while gaining international experience and extending open-mindedness in a new cultural environment. • Built additional knowledge in cryptography and ethical hacking fundamentals.