introduction
Medical image analysis plays an important role in modern medical diagnosis. With the rapid development of deep learning technology, we are now able to use powerful neural networks to automatically identify, segment and diagnose medical images, such as MRI (magnetic resonance imaging) and CT (computed tomography) images. This blog will introduce in detail how to use R language and deep learning for medical image analysis, as well as its potential applications in the medical field.
Part One: Data Acquisition and Understanding
Before performing medical image analysis, the first priority is to obtain appropriate medical imaging data. This data typically includes MRI or CT scan images, along with corresponding labels or diagnostic information. In this blog, we will use a publicly available medical imaging dataset as an example.
First, load the required R language libraries and download the sample data set.
# 加载所需的库
library(reticulate)
library(keras)
library(imager)
library(tidyr)
# 使用Python下载示例数据集（可替换为自己的数据集）
py_run_string("import urllib.request
urllib.request.urlretrieve('https://example.com/medical_images.zip', 'medical_images.zip')")
# 解压数据集
py_run_string("import zipfile
with zipfile.ZipFile('medical_images.zip', 'r') as zip_ref:
zip_ref.extractall('medical_images')")
Next, we'll read and understand medical imaging data.
# 读取示例MRI图像和标签
image_path <