Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring System in Big Data

Abstract

Medical image classifiers roles a crucial play in medical service and teaching tasks. But the classical approach obtained its ceiling on performance. Besides, from their use, much longer and more effort require spent on extracted and selected classifier features. The Deep Neural Network (DNN) is a developing Machine Learning (ML) approach which is verified their potential for distinct classifier tasks. Especially, the Convolutional Neural Network (CNN) leads to optimum outcomes on distinct image classifier tasks. But medical image databases can be hard for collecting as it requires several professional skills to categorize them. This study develops a new Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring Systems (HPTDLM-HMS) in big data environment. The presented HPTDLM-HMS technique concentrates on the examination of medical images in the decision-making process. Initially, the presented HPTDLM-HMS technique derives features using EfficientNet model with Manta Ray Foraging Optimization (MRFO) algorithm as hyperparameter tuner. At last, the classification of medical images takes place by Long Short-Term Memory (LSTM) method. To handle big data, Hadoop MapReduce is utilized. The result analysis of the HPTDLM-HMS technique is tested on medical imaging dataset. The comprehensive study of the HPTDLM-HMS technique highlighted and gives recall value of 87.46% is higher when compared to its promising outcomes over other models.