Best Algorithm for Brain Tumor Detection
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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?
Based on the problem statement, the research team needs a machine learning algorithm that can accurately detect brain tumors in millions of brain scans and can perform at scale to handle the high volume of incoming scans. The consequences of false negative predictions are severe, so the accuracy of the model is critical.
Out of the given options, the algorithm that best fits this problem is C. Image Classification.
Image classification is the process of categorizing images into different classes based on their features or content. In this case, the algorithm would be trained on millions of brain scans, with each scan labeled as either having a tumor or not. The algorithm would then be able to classify new brain scans as either having a tumor or not with high accuracy.
The benefits of using image classification for this problem are:
Accuracy: Image classification algorithms have been shown to perform well on a wide range of tasks, including medical imaging. With the right training data and algorithm, the model can achieve high accuracy in detecting brain tumors, reducing the number of false negative predictions.
Scalability: Image classification algorithms can be trained on large datasets, which is essential in this case since the research team has millions of brain scans. Additionally, image classification algorithms can be deployed on distributed systems to handle the high volume of incoming scans.
Interpretability: Image classification algorithms can provide insights into which features are most important for detecting brain tumors. This information can be used to improve the accuracy of the algorithm or to inform medical practitioners of the features to look for in brain scans.
Therefore, Image Classification is the best option out of the given alternatives for the problem at hand.
