Building a medical image classification model using SageMaker
One common application of ML in medical imaging is for classifying images into different categories. These categories can consist of different types of diseases determined by visual biomarkers. It can also recognize anomalies in a broad group of images and flag the ones that need further investigation by a radiologist. Having a classifier automate the task of prescreening the images reduces the burden on radiologists, who can concentrate on more complex and nuanced cases requiring expert intervention. In this exercise, we will train a model on SageMaker to recognize signs of pneumonia in a chest X-ray. Let us begin by acquiring the dataset.
Acquiring the dataset and code
The Chest X-Ray Images (Pneumonia)
dataset is available on the Kaggle website here: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia?resource=download.
The dataset consists of 5,863 chest X-ray images in JPEG format organized...