Advancing Automated Medical Image Diagnosis Through Deep Learning Framework Integration

Medical image analysis is an essential part of modern healthcare, helping to identify, manage, and track a wide range of medical issues. The increasing volume and complexity of medical imaging data necessitate advanced tools and technologies to automate and improve the diagnostic process. Deep learning frameworks, such as 3D Slicer, MONAI, SAM, and YOLOv8, have emerged as powerful solutions that utilize artificial intelligence to analyze and interpret medical images accurately and efficiently. This study focuses on integrating these four prominent deep learning frameworks into automated medical image diagnosis, offering capabilities ranging from image segmentation to real-time visualization and object detection. By leveraging the computational power of Deep Neural Networks (DNN), the healthcare professionals can extract valuable insights from complex medical imaging datasets, leading to better decision-making and patient outcomes. Evaluating and comparing these frameworks is crucial for understanding their strengths and limitations in medical image analysis. 3D Slicer excels in interactive 3D visualization and segmentation, while MONAI provides an end-to-end solution for medical data processing. SAM Modal is known for its adaptability in managing segmentation tasks, especially in liver lesion identification. YOLOv8 shows promise in detecting abnormalities and tumors through its object detection capabilities.