Title of article :
A new approach using Machine Learning and Deep Learning for the prediction of cancer tumor
Author/Authors :
Asgari, Fatemeh Department of biomedical engineering - Islamic Azad University Isfahan (Khorasgan) Branch, Isfahan, Iran , Minooei, Arian Department of biomedical engineering - Islamic Azad University Isfahan (Khorasgan) Branch, Isfahan, Iran , Abdolahi, Somayeh Department of biomedical engineering - Islamic Azad University Isfahan (Khorasgan) Branch, Isfahan, Iran , Shokrani Foroushani, Reza School of Medicine - Isfahan University of Medical Sciences, Isfahan, Iran , Ghorbani, Atefeh Biotechnology Department - Islamic Azad University Falavarjan Branch, Isfahan, Iran
Pages :
11
From page :
41
To page :
51
Abstract :
Cancer is now one of the leading causes of death in the world. Existing therapies such as chemotherapy, radiation therapy and other methods due to the effect on other parts and even some side effects are irreversible. On the other hand, the development of nanotechnology to the treatment of cancer has been used in various fields especially drug delivery and diagnostic and imaging cases. Because of their similarity to biological molecules, nanoparticles can easily enter cells and receive much attention when it comes to drug delivery to target tissues. Also, the best and effective solution for treatment is the use of multidisciplinary approaches, and on the other hand, one of the challenges in medical image recognition methods is the problem of dense tissue analysis and the time-consuming process of diagnosis which has caused machine learning and deep learning to receive much attention. Deep learning techniques use data stored in electronic health systems files and process large amounts of resources such as medical imaging and artificial neural networks to help physicians analyze information and diagnose multiple diseases.
Keywords :
Machine learning , Deep learning , Nanoparticles , Medical equipment , Thermotherapy
Journal title :
Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering
Serial Year :
2021
Record number :
2724409
Link To Document :
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