[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120666-en":3,"doc-seo-120666-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120666,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",7,"Healthcare","Sustainable and Reliable Healthcare Automation and Digitization using Machine Learning Techniques - Deep Learning for Brain Tumor Phase Prediction","Healthcare 4.0 aligned with Industry 4.0 leverages IIoT, automation, and digitalization, with machine learning for forecasting and prediction. The paper explores deep learning methods to predict the phase of brain tumors, enabling clinicians to relate patient status to similar cases and anticipate future anomalies. Popular public datasets support modeling, while supervised machine learning is highlighted for handling complex patterns. Python-based simulations evaluate performance using standard metrics for tumor image classification.","Journal of Scientific & Industrial Research Vol. 82, February 2023, pp. 226-231 DOI: 10.56042/jsir.v82i2.70222  \nSustainable and Reliable Healthcare Automation and Digitization using  \nMachine Learning Techniques  \nB V D S Sekhar1*, Bh V S Ramakrishnam Raju1, N Udaya Kumar2 & VVSSS Chakravarthy3  \n1Department of IT, 2Department of ECE, S R K R Engg college, Bhimavaram 534 204, Andhra Pradesh, India 3Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam 531 162,  \nAndhra Pradesh, India  \nReceived 26 June 2022; revised 23 September 2022; accepted 07 October 2022  \nHealthcare 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.  \nKeywords: Brain tumor, Deep learning, Healthcare 4.0, Industry 4.0, Sustainable technology  \nIntroduction  \nHealthcare automation is much needed to meet the challenges like remote diagnosis and medication. The advancements in medical imaging led to efficient human organ analysis, which significantly contributed to respectable and influential diagnosis and further treatment. These advanced imaging techniques have become a handy tool for radiologists. Out of several organs, the brain makes up the core part, and this organ regulates the nervous system. Brain tumor isone of the foremost common and, therefore, the deadliest brain diseases that have affected and ruined several lives worldwide. Cancer is a disease in the brain in which cancer cells ascend in brain tissues. According to a new study on cancer, more than one lakh people are diagnosed with brain tumors annually around the globe. Regardless of stable efforts to overcome the complications of brain tumors, figures show unpleasing results for tumor patients.  \nThe diseases affecting different body organs maybe limited to one organ or spread to another.  \n*Author for Correspondence [E-mail: bvdssekhar@gmail.com](E-mail: bvdssekhar@gmail.com)  \nHowever, most brain diseases impact the functioning of other organs and may put the patients in dangerous situations and lead to death.1 So, the identification and treatment planning of brain diseases is an important task. Automating medical image diagnosis and analysis plays a vital role in treatment decisions. Analysis part of medical images is done by processing them through some image processing techniques.  \nSeveral algorithms are proposed to classify biomedical images for effective digitalization, automation, and diagnosis. An expert system approach is presented using type-II fuzzy logic for classification. The proposed technique proved to be better in classifying brain tumors and valuable for diagnosis.2  \nThe work presented in this paper corresponds to the implementation ","cbCaitVZFyQIlL5x","https://ap.wps.com/l/cbCaitVZFyQIlL5x","pdf",7371371,1,6,"English","en",105,"# Introduction\n## Healthcare automation and the need for digitization\n## Brain tumor challenges and diagnosis importance\n# Literature Survey\n## Feature extraction, clustering, and fuzzy approaches\n## Prior results and performance metrics\n# Methodology and Implementation\n## Neural networks and supervised classification framework\n## Dataset, tools, and simulation setup\n# Evaluation and Classification Goals\n## Tumor types: glioma, meningioma, pituitary\n## Metrics such as accuracy and loss","[{\"question\":\"What is the main goal of the proposed healthcare automation approach?\",\"answer\":\"The approach aims to support sustainable, reliable healthcare automation and digitization by using machine learning for prediction and diagnosis support, specifically targeting brain tumor image analysis.\"},{\"question\":\"Which deep learning task does the paper focus on for brain tumors?\",\"answer\":\"It focuses on predicting the phase of brain tumors and correlating patient status with similar subjects to assess and predict future anomalies.\"},{\"question\":\"How is the brain tumor classification implemented and evaluated?\",\"answer\":\"The study uses a Python simulation with machine learning libraries such as Keras and sklearn, and it evaluates results using standard metrics like accuracy and loss.\"}]","Sustainable and Reliable Healthcare Automation and Digitization using Machine Learning Techniques - 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