You work as a machine learning specialist for a medical research facility. Your research team is working on a brain tumor detection scanner to be used in hospitals across the country. The team has decided to use machine learning to detect tumors in the scans and to catalog the findings in a database that can be shared across medical facilities.
You have millions of brain scan data to use in your model. Also, you will have an incoming stream of new scans every day, so your volume is very high. Your research team requires that the model performs at scale and with very high accuracy due to the nature of the consequences of false negative predictions.
Which algorithm best fits your problem?

Answer options:

A.Object Detection
B.K-Means
C.Image Classification
D.Random Cut Forest

Answer: C
Option A is incorrect. The Object Detection algorithm is used to identify all instances of an object within an image. You are trying to classify a high-resolution image as either containing a tumor or not. You are not trying to identify and surrounding all elements in an image with a bounding box.
Option B is incorrect. The K-Means algorithm is used to find groups within data where the members of the group are similar. This would not work for our image classification problem.
Option C is correct. The SageMaker built-in Image Classification algorithm uses a Convolutional Neural Network to classify images. It breaks up each image into a series of tiles and then predicts what each tile contains. This is the optimal way to find a tumor within a larger brain scan image. (See the article Image Classification using Deep Neural Networks - A beginner friendly approach using TensorFlow)
Option D is incorrect. The Random Cut Forest algorithm is used to find abnormal data points with your dataset. It would not be the best choice for your image classification problem with large numbers of high-resolution images in which you are trying to detect an anomaly.
Reference:
Please see the SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms, and the article titled How might companies use random forest models for predictions?