Learn how to build your own AI radiologist with this step-by-step guide! From environment setup to training a VGG16 model to detect Pneumonia in medical images.
Radiology is a field that significantly depends on the expertise of highly skilled professionals. Radiologists analyse medical images to diagnose and monitor a range of conditions, from simple fractures to complex diseases such as cancer. Yet, with the surge in medical imaging and the urgent need for fast, accurate diagnoses, radiologists are under tremendous pressure. This is where artificial intelligence steps in, transforming the field by enhancing human capabilities.
Specifically, we'll import Keras to use as our high-level API, Sequential for constructing a linear stack of layers, and modules like Dense, Conv2D, MaxPool2D, and Flatten to build and configure various neural network layers. Additionally, ImageDataGenerator will help us augment our image data, enhancing the model's ability to generalize. Lastly, we'll import numpy for its numerical operations support, particularly useful for handling arrays and performing mathematical functions.
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