COMPREHENSIVE DOCUMENT INFORMATION RETRIEVAL USING DEEP LEARNING

Abstract

Retrieving relevant medical data promptly is essential for both improving patient care and scientific discoveries. To compare the performance of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Neural Networks (DNNs), and a unique fusion approach called Random Multimodel Deep Learning (RMDL), this project addresses the application of deep learning models in medical document retrieval. The findings of this project emphasize the value of RMDL's multi model fusion and imply that integrating several learning modalities can have positive effects in the medical field. Improved document retrieval systems have the potential to provide medical personnel with faster access to pertinent information, which could result in more precise diagnoses, more focused treatment regimens, and ultimately improved patient outcomes. Improved retrieval also makes it easier to collect information for medical research in an efficient manner, which speeds up breakthroughs across a range of domains and aids in the creation of individualized medicine strategies. Ultimately, the most effective model is assessed using performance evaluation parameters. Using deep learning models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Neural Networks (DNN), and Random Multimodel Deep Learning (RMDL), the study also provides an extensive examination of Document Information Retrieval (DIR). The project carefully analyses and assesses the accuracy of these individual models and ensemble strategies in DIR tasks, drawing on a close reading of more than 25 research papers.