Sağlık Alanında Kullanılan Derin Öğrenme Yöntemleri

Uzun süreli tedavi gerektiren kanser ve benzeri hastalıklara yakalanan hastaların ölüm riski yüksektir. Bu riski azaltmak ve hastanınyaşam süresini uzatmak için tıpta, teknolojideki gelişmelerin de kullanıldığı çalışmalar bulunmaktadır. Bu çalışmalarda hastalığıntedavisi için çok önemli olan erken tanı yöntemlerine odaklanılmıştır. Yapay zekâ, makinelerin insan beyninin çalışmasını taklitederek karar verme ve tahmin etme gibi çözülmesi zor olan problemlerin çözümüne imkân tanıyan bir bilim dalıdır. Yapay zekânın biralt dalı olan makine öğrenmesi ise kodlanmış olan hazır talimatları kullanarak çözüm üretmek yerine; örneklerden öğrenerek, görüntü,resim ve ses tanıma gibi birçok zor probleme çözüm getirmektedir. Son yıllarda birçok alanda kullanılan makine öğrenmesinin,hastalıkların erken teşhisinde kullanılabilme potansiyeli de bulunmaktadır. Konu ile ilgili yapılan çalışmalar özellikle makineöğrenmesinin bir alt dalı olan derin öğrenme yöntemlerine odaklanmıştır. Bu çalışmanın amacı sağlık alanında uygulanan derinöğrenme yöntemlerinin çalışma prensiplerini ve hangi hastalıklarda kullanıldığını, ilgili literatür ışığında ortaya koymaktır. Buçalışmanın sonucunda, hastalığın teşhisinde kullanılan verilere uygun derin öğrenme yönteminin tercih edilmesinin, hastalığa erkentanı konma başarısını arttıracağı düşünülmektedir.

Deep Learning Methods used in the field of Health

Patients with cancer and similar diseases requiring long-term treatment have a high risk of death. In order to reduce this risk and extend the life expectancy of the patient, there are studies that use advances in technology in medicine. These studies focused on early diagnosis methods which are very important for the treatment of the disease. Artificial intelligence is a branch of science that allows machines to solve problems that are difficult to solve, such as making decisions and predictions, by imitating the workings of the human brain. Machine learning, which is a sub-branch of artificial intelligence, instead of producing solutions by using ready-coded instructions; learning from examples, it provides solutions to many difficult problems such as image, picture and voice recognition. Machine learning, which has been used in many areas in recent years, has the potential to be used in early diagnosis of diseases. Studies on the subject have focused on deep learning methods, which is a sub-branch of machine learning. The aim of this study is to reveal the working principles of deep learning methods applied in the field of health and in which diseases they are used in the light of the related literature. As a result of this study, it is thought that choosing a deep learning method appropriate to the data used in the diagnosis of the disease will increase the success of early diagnosis of the disease.

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