A Survey on the deep learning architectures in the field of bio-medical engineering

AUTHOR(S)

Deekshitha P., Dr. T.C.Manjunath

DOI: https://doi.org/10.46647/ijetms.2023.v07i03.103

ABSTRACT
Now a day’s tumor is second leading cause of cancer. Due to cancer, large no. of patients are in danger. The medical field needs fast, automated, efficient and reliable techniques to detect tumors like brain tumors. Detection plays a very important role in treatment. If proper detection of tumor is possible then doctors keep a patient out of danger. Various image processing techniques are used in this application. Using this application doctors provide proper treatment and save a number of tumor patients. A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. A tumor is a mass of tissue it grows out of control. We can use a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) and VGG 16(visual geometry group) Transfer learning for detecting brain tumors. The performance of the model is predicting image tumor is present or not in image. If the tumor is present, it returns yes otherwise return no. The work done is the third semester project phase-1 & 2 as a part of the M.Tech. project work in the 2nd year.
Page No: 653 - 656

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Deekshitha P, Dr. T C Manjunath “International Journal of Engineering Technology and Management Sciences” ISSN: 2581-4621), February 2023 Third International Conference on advances in Science & Technology, ICOST-2023 organised by RSP conference Hub, Coimbatore, Tamilnadu, India.

How to Cite This Article:
Deekshitha P., Dr. T.C.Manjunath .A Survey on the deep learning architectures in the field of bio-medical engineering . ijetms;7(3):653-656. DOI: 10.46647/ijetms.2023.v07i03.103