Deep Learning for Integrative Healthcare Analytics:A Focus on Multimodal Brain Imaging Data

Abstract

Medical imaging faces several significant challenges, including image segmentation, cross-modal translation, and real-value
prediction. Link CT and MRI scans using a well-liked method. Image quality and diagnostic efficacy can be enhanced with this
technique. One new area of data science is brain imaging genetics, which aims to better understand the brain's normal and abnormal
phenotypic, molecular, and genetic features and how they influence its function and behaviour. Enhancing diagnostic accuracy in
neurological diseases, this work proposes a multimodal approach to analyzing brain tumor imaging data for healthcare analytics. It
leverages modern predictive models. Multiple models were tested using extensive data, such as CNN, Report Guided Net, MLP, and
ResNet18. With an F1-score of 99.12%, recall of 99.67%, precision of 99%, and accuracy of 99.28%, the Convolutional Neural
Network (CNN) model stood out from the contest. These results prove that deep learning techniques, such as CNNs, can understand
complex multimodal images and extract valuable information from them. The results provide credence to the idea that these models
can be useful in automating the identification of brain disorders by incorporating them into clinical procedures.