The integration of machine learning (ML) tech- niques in medical imaging has revolutionized the automated de- tection of tumors, enabling earlier diagnosis and improved patient outcomes. This paper explores the application of ML, particularly deep learning algorithms such as convolutional neural networks (CNNs), in detecting tumors in various imaging modalities, including MRI, CT, and X-ray. We discuss the methodologies, challenges, and advancements in ML-driven tumor detection, focusing on data preprocessing, model architectures, performance evaluation, and case studies. The paper also examines ethical considerations, limitations, and future directions for ML in medical imaging. Our analysis highlights the potential of ML to enhance diagnostic accuracy while addressing challenges such as data scarcity, model interpretability, and ethical biases....
Authors: Pankaj Shrotriy, Dr. Pradeep Yadav.