Anomaly Detection in Medical Imaging: Bridging the Gap between 2D Processing and Interactive 3D Visualization

This paper mainly aims to present the use of transform a 2D image into a 3D interactive model which will help us visualize the anomaly, allowing the medical sector a better understanding of anomalies in medical images, which may even unearth new findings. Using stable diffusion algorithms, the generative model and computer vision converts 2D images into interactive 3D models. Existing structures usually struggle with complex 2D images when the anomaly is detected, hence focus on building a 3D model for deeper understanding is necessary. In this model, the challenges and roadblocks in normal 2D images for anomaly detection and why a 3D model will provide a better insight into the anomaly from medical images is discussed. Next, it explores the technological benefits of having an advanced 3D model and how the 3D model will help in interacting with the anomaly by employing 3D models to show the expected progression in the anomaly extraction process. Finally outlining the previously implemented models to generate 3D interactive objects such as, generative adversarial networks (GANs) and convolutional neural networks (CNNs), as they have exhibited promising results in extracting meaningful features and detecting anomalies in medical images. It emphasizes the importance of bridging the gap between 2D image processing and interactive 3D visualization in anomaly by incorporating advanced techniques, leveraging 3D information, and enabling interactive exploration, that enhance the precision and efficacy of anomaly detection, aiding in achieving quicker diagnosis and treatment as outcomes in healthcare.