Abut, SerdarOkut, Hayrettin2024-12-242024-12-242024979-889113725-7979-889113665-6https://hdl.handle.net/20.500.12604/4090This book chapter explores the integration of machine learning techniques, particularly deep neural networks, in the field of medical image processing for precision medicine. The healthcare industry has accumulated vast amounts of complex data, and advancements in technology have led to an increase in structured and unstructured medical data. The chapter discusses the historical development of image processing techniques, moving from labor-intensive approaches to more efficient and faster operations using artificial neural networks. Various feature extraction methods, with a focus on dimensionality reduction, are investigated to optimize the performance of neural networks. The application of deep neural network models in medical imaging is explored, with a gradual implementation strategy proposed to address challenges related to data variability across institutions. The potential benefits and obstacles of using deep neural network models for psychiatric diagnoses and neonatal early detection are discussed. Throughout the chapter, the importance of effective communication between data scientists, software engineers, and clinicians is emphasized for the development of robust and practical artificial intelligence systems in healthcare. The overall viewpoint highlight how technological advancements in image processing have transformed artificial intelligence-based applications in the medical field and future developments can be looked at in a different light in this area. © 2024 by Nova Science Publishers, Inc. All rights reserved.eninfo:eu-repo/semantics/closedAccessArtificial neural networkConvolutional neural networkDeep learningMedical image processingBook Chapter124N/A2-s2.0-85195721356