A new approach using Machine Learning and Deep Learning for the prediction of cancer tumor
الموضوعات :
فصلنامه شبیه سازی و تحلیل تکنولوژی های نوین در مهندسی مکانیک
Fatemeh Asgari
1
,
Arian Minooei
2
,
Somayeh Abdolahi
3
,
Reza Shokrani Foroushani
4
,
Atefeh Ghorbani
5
1 - Department of biomedical engineering, Isfahan (khorasgan)Branch, Islamic Azad University, Isfahan, Iran
2 - Department of biomedical engineering, Isfahan (khorasgan)Branch, Islamic Azad University, Isfahan, Iran
3 - Department of biomedical engineering Isfahan(Khorasgon) Branch, Islamic Azad University, Isfahan, Iran
4 - School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
5 - Biotechnology Department, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
تاريخ الإرسال : 05 الأحد , رجب, 1443
تاريخ التأكيد : 15 السبت , رمضان, 1443
تاريخ الإصدار : 26 الإثنين , ربيع الأول, 1443
الکلمات المفتاحية:
Machine Learning,
nanoparticles,
deep learning,
Medical equipment,
Thermotherapy,
ملخص المقالة :
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 that in some cases are irreversible, has led researchers to find a new and more specific method with fewer side effects. On the other hand, the development of nanotechnology, in addition 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 (artificial intelligence) 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. And makes the diagnosis automatically
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