Developed a CNN-based model to detect melanoma with an impressive accuracy of 0.81, demonstrating strong proficiency in machine learning and deep learning techniques. Leveraged data augmentation techniques to effectively address class imbalance, ensuring the model's robustness and generalizability across diverse datasets. This project highlights my ability to apply advanced AI methodologies to real-world problems, showcasing my expertise in handling complex datasets and enhancing model performance. My work not only underscores my technical skills in CNN architecture but also reflects my commitment to delivering high-quality, impactful solutions in the field of medical diagnostics.