Sustainable and Reliable Healthcare Automation and Digitization using Machine Learning Techniques

Abstract

Healthcare 4.0 takes significant benefits while aligned with Industry 4.0. Mainly citing the recent and existing pandemic, the need for Industry Internet of Things (IIoT), automation, digitalization, and induction of machine learning techniques for forecasting and prediction have been the technologies to rely on. On these lines, digitization and automation in the healthcare industry have been practical tools to accelerate diagnosis and provide handy second opinions to practitioners. Sustainability in health care has several objectives, like reduced cost and low emission rate, while promising effective outcomes and ease of diagnosis. In this paper, such an attempt has been made to employ deep learning techniques to predict the phase of brain tumors. The deep learning methods help practitioners to correlate patients' status with similar subjects and assess and predict future anomalies due to brain tumors. Popular datasets have been employed for modeling the prediction process. Machine learning has been the most successful tool for handling supervised classification while dealing with complex patterns. The study aims to apply this machine learning technique to classifying images of brains with different types of tumors: meningioma, glioma, and pituitary. The simulation is performed in a python environment, and analysis is carried out using standard metrics.