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Journals / Acta Infologica / 2021 / Cilt: 5 - Sayı: 2

Deep Learning Applications in Medical Image Processing

Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları

Nevcihan DURU
Ayşe Gül EKER
Pages
459–474
Publication Date
2022-09-04
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our aim is for this study to guide different studies on medical images with deep learning methods and serve as a basic resource in this field.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Medical imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), mammography, ultrasound and x-ray have been used for the diagnosis and treatment of diseases for many years.
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Key Findings & Data:With the increase in the amount of data, machine learning methods have remained insufficient in the field of image processing.
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Conclusion & Impact:Our aim is for this study to guide different studies on medical images with deep learning methods and serve as a basic resource in this field.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Domain Parameters

Abstract

Medical imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), mammography, ultrasound and x-ray have been used for the diagnosis and treatment of diseases for many years. However, machine learning methods are used in this field for reasons such as earlier diagnosis of diseases, reduce the workload of doctors, and adjudicate conflicting expert opinions. With the increase in the amount of data, machine learning methods have remained insufficient in the field of image processing. Thanks to the developing mathematical models and hardware devices, deep learning has taken a wide place in this field. In this study, the application of deep learning methods in the field of medical image processing has been examined. Very recent examples are presented from studies in the fields of segmentation, classification and disease diagnosis, image generation, image transformation and image enhancement. The algorithms used in the studies are briefly explained. In addition, brain tumor segmentation with deep learning was tried on the BraTS 2020 dataset, and as a result, a dice similarity rate of 86% and a sensitivity value of 80% were obtained. Our aim is for this study to guide different studies on medical images with deep learning methods and serve as a basic resource in this field.

Özet

Manyetik rezonans görüntüleme (MRI), bilgisayarlı tomografi (BT), pozitron emisyon tomografisi (PET), mamografi, ultrason ve röntgen gibi tıbbi görüntüleme teknikleri uzun yıllardan beri hastalıkların teşhisi, tanısı ve tedavisi için kullanılmıştır. Ancak hastalıkların daha erken teşhisi, uzmanların yoğunluğunu azaltma, çakışan uzman görüşlerini karara bağlama gibi nedenlerle, bu alanda makine öğrenmesi yöntemlerinden yararlanılmaktadır. Veri miktarının artması ile makine öğrenmesi yöntemleri görüntü işleme alanında yetersiz kalmış, gelişen matematiksel modeller ve donanımsal cihazlar sayesinde derin öğrenme bu alanda kendine geniş bir yer edinmiştir. Bu çalışmada derin öğrenme yöntemlerinin medikal görüntü işleme alanında uygulanması incelenmiştir. Segmentasyon, sınıflandırma ve hastalık teşhisi, görüntü oluşturma, dönüştürme ve iyileştirme alanlarında yapılan çalışmalardan oldukça güncel örnekler sunulmuş, yapılan çalışmalarda kullanılan algoritmalar kısaca açıklanmıştır. Ayrıca BraTS 2020 veri seti üzerinde derin öğrenme ile beyin tümör segmentasyonu gerçekleştirme denenmiş, sonuç olarak %86 dice benzerlik oranı ve %80 hassasiyet değeri elde edilmiştir. Bu çalışmanın medikal görüntüler üzerinde derin öğrenme yöntemleri ile yapılacak farklı çalışmalara yol gösterecek bir kaynak olması hedeflenmiştir.

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Cite This Article

DURU, N., EKER, A. G. (2022). Deep Learning Applications in Medical Image Processing. Acta Infologica, Cilt: 5 - Sayı: 2, 459–474. https://ajindex.com/articles/425967

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