# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 5 Applications in Healthcare from Biomedical Information
### Biomedical Images  
Medical imaging refers to the processes that provide visual information of the human body with the purpose of aiding physicians to make diagnostics and treatments more efficient [115]. The most widespread applications of DL that used medical data are just the medical image processing applications, and it is due mainly to the success of this approach in computer vision [48; 51] and the success of CNN architecture for image analysis. To this should be added that medical images are analyzed manually by physicians, who are limited by speed, fatigue, and experience. Incorrect diagnoses or delays can cause harm to the patient. Therefore, there is a necessity of automated, accurate and efficient medical image analysis to help physician research, and this is being widely developed in this field [51; 116].  
Some important medical image applications are classification of diseases, lesions, or abnormalities, and segmentation of regions of interest (ROIs) such as tissues and organs. Classification (also known as Computer-Aided Diagnosis [CADx] [116]) is the major issue in medical image analysis, though segmentation of ROIs in medical imaging is fundamental for qualitative and quantitative assessment [19]. Other important tasks are location, detection (also known as Computer-Aided Detection [CADe] [116]), and registration. Localization focuses on drawing a bounding box around a single object in the image; meanwhile, detection concerns drawing bounding boxes around multiple objects (which can be from different classes) in the image, and classifying each object. Registration refers to fitting one image onto another [116; 117; 118].  
Each technology gives different properties imaged about the area of the body being studied and they vary depending on the imaging modality: ultrasound (US), X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), retinal photography, histology slides, and dermoscopy images [116; 119]. These, together with typically small datasets in some modalities and disciplines, annotations that can be scarce, and images which are often high-dimensional, multimodal and multi-channel [20; 116] make the medical imaging a well-suited field to challenges.  
DL solutions have been applied to medical image analysis [120] in very different topics related to classification or detection, such as identifying lung nodules into benign or malignant [121], alcoholism identification through brain MRI [122; 123], multiple sclerosis detection [124], categorizing chest X-rays into different diseases [125], distinguishing patients with Alzheimer's disease versus normal [126], diagnosing diabetic retinopathy using digital photographs of the fundus of the eye [127], discriminating kidney cancer histopathological images into tumor or non-tumor [128], detecting cancerous lung nodules [129] and lung cancer stage [130] on CT scans, malignant skin cells on dermatological photographs [131], mitotic figures in breast histology images [132], or cell nuclei in colorectal adenocarcinoma histology image [133]. Regarding image segmentation, DL covers a variety of organs such as liver, prostate, spine, and knee cartilage both in CT and MRI [116; 134].  
Many studies have focused on brain segmentation, including tumor segmentation [135]. Currently, interesting niche areas [116] are being explored in medical images registration, such as those that seek spatial relationships between two or more images (deformable image registration [DIR]) [136].