Identifying the Most Effective Machine Learning or Deep Learning Algorithm for Accurately Diagnosing to Improve Patient Outcomes

Accurate diagnosis and classification of ovarian cysts from medical imaging data is essential for providing better outcomes to patients and avoiding diagnostic errors. In this study, different machine learning and deep learning algorithms are evaluated to identify the best algorithm for diagnosing and classifying ovarian cysts. The study implements and compares aforementioned models on top of a dataset containing medical imaging data; SVM, Random Forests, CNNs, and advanced deep learning architectures are used. The performance evaluation metrics for each model are evaluated by accuracy,precision,recall,F1score.Thepreliminary results demonstrate that both diagnostic accuracy and reliability of deep learning approaches, namely CNN based architectures, outperform traditional machine learning models. These findings are meant to contribute to the development of robust diagnostic devices to aid in early detection and optimize clinical decision-making for ovarian cysts.