Deep Learning Performance on Medical Image, Data and Signals

In this study, the recent medical studies with deep learning between 2009-2019 have been researched for observing the performance of deep learning on medical images, data and signal. Recent studies attained from Web of Science have been evaluated and selected according to the citation numbers. Studies have been listed as a table, according to the publication year, deep network structure, database used training and testing, evaluation metric and results. The studies have also been classified into the organs and the types of important diagnosis. The results have shown that the deep learning network structures, applied on fundus images, have attained nearly %99 percent accuracy. Although most of the studies between the range, made by Radiology and Nuclear Medicine Imaging, the accuracy of the results are 80-90% range. The current studies especially focus on automatic detection or classification of the tumor as benign or malign. Studies are mostly on medical CT, ultrasound, radiography and MRI images. This results show that computer aided medical diagnosis systems will be used in a very near future with fully performance.

Tıbbı Görüntü, Veri ve Sinyaller Üzerinde Derin Öğrenme Performansları

Bu çalışmada, 2009-2019 yılları arasında Tıpta derin öğrenme ile ilgili yapılmış çalışmalar, derin öğrenmenin Tıbbı görüntü, veri ve sinyaller üzerine başarısını gözlemlemek için araştırılmıştır. Web of Science’tan elde edilen çalışmalar değerlendirilmiş ve atıf sayısına göre seçilmişlerdir. Çalışmalar yayın yılı, derin ağ yapısı, kullanılan veritabanı ve değerlendirme kriterine göre tablo haline getirilmiştir. Çalışmalar organlara göre ve tanılara göre de sınıflandırılmıştır. Sonuçlar retinal fundus görüntüleri uygulanan derin öğrenme ağ yapılarının doğruluklarının %99’lara ulaştığını göstemektedir. Bu aralıktaki çalışmaların çoğu radyoloji ve nükleer tıp alanında yapılmış olsa de sonuçlar henüz %80-90 aralığında görülmektedir. Yapılan çalışmalar özellikle tümörlerin otomatik tesbiti veya tümörlerin iyi veya kötü huylu olarak sınıflandırılması üzerinedir. Çalışmalar çoğunlukla tıbbı tomografi, ultrases, radyografi ve manyetik resonans görüntüler üzerinedir. Bu sonuçlar bilgisayar destekli teşhis sistemlerinin çok yakın bir gelecekte tam performans ile kullanılacağını göstermektedir.

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