Open AccessMedical Image Analytics using Deep Learning (Convolutional Neural Networks)
Author(s) -
Santosh Bothe,
Mrunmayee Inamke,
Uttara Patidar,
Rutvi Ordia
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206291
Subject(s) - deep learning , convolutional neural network , health care , computer science , artificial intelligence , analytics , field (mathematics) , medical imaging , machine learning , data science , mathematics , pure mathematics , economics , economic growth
Technical developments are being done in medical field. In order to improve medical results and healthcare facilities, machine learning and deep learning concepts are being used. Various experiments and efforts are done to detect diseases and provide platforms to provide better healthcare. Involvement of technology has made healthcare field more efficient and trustworthy. The ‘Medical Image Analytics’ is a machine learning as well as deep learning tool that would provide platform for processing medical images and extracting features not visible to human eye and provide accurate results and help to healthcare organizations. It strives to help healthcare organization for providing better healthcare facilities. This project is intended for use in various healthcare fields and organizations. Some features of the disease in medical images can be nit invisible or not clear to human eyes. Improper detection of features can lead to improper detection of diseases and may lead to failure or degradation in health and healthcare facilities. Thus, using techniques like deep learning and machine learning increases the detection of features in medical images. Also, it is helpful if diseases can be detected at an early stage and therefore, the project would aim to detect diseases at an early stage in future.