Review Research of Medical Image Analysis Using Deep Learning

Abstract

In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

A review of machine learning applications for the proton MR spectroscopy workflow

“…3 The ability to learn model-agnostic features from data has made machine learning (ML) methods very popular in many disciplines over the last decade. In MRI the use of ML techniques, for example, has increasingly found a wide range of applications ranging from image reconstruction [8][9][10] and quality improvement 11 to image analysis 12 and clinical diagnostics. [13][14][15][16] This trend has started to increase in MRS and MRSI as well, with various ML methods being proposed to address some of the associated challenges.…”