An Automated Medical Image Segmentation Framework using Deep Learning and Variational Autoencoders with Conditional Neural Networks
Dustakar Surendra Rao, L. Koteswara Rao, Vipparthi Bhagyaraju · International Journal of Advanced Computer Science and Applications · 2023
It is a highly difficult challenge to achieve correlation between images by reliable image authentication and this is essential for numerous therapeutic activities like combining images, creating tissue atlases and tracking the development of the tumors. The separation of healthcare data utilizing deep learning variational autoencoders and conditional neural networks is presented in this research as a paradigm. One of the essential jobs in machine vision is the partitioning of an image. Due to the requirement for low-level spatial data, this assignment is more challenging compared to other vision-related challenges. By utilizing VAEs' capacity to develop hidden representations and combining CNNs in a conditioned environment, the algorithm generates accurate and efficient results for the segmentation. Moreover, to learn the representation of latent space from labelled clinical images, the VAE is trained as part of the system that is suggested. After that, the representations that were learned and real categorizations are used to develop the conditional neural network. Furthermore, the model that has been trained is utilized to accurately separate the areas that are important in brand-new medical images during the inferential stage. Thus, the experimental findings on several healthcare imaging databases show the enhanced precision of segmentation of the structure, highlighting its ability to enhance automated diagnosis and treatment. Henceforth, the suggested Deep Learning and Variational Auto Encoders with Conditional Neural Networks (DL-VAE-CNN) are employed to solve the pixel-level problem of classification that plagues the earlier investigations.