Convolutional Neural Networks (CNN) Basics

Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective for analyzing visual imagery. They are widely used in fields such as image recognition, object detection, and medical image analysis.

What is a CNN?

CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input images. The architecture of a CNN is inspired by the visual processing system of the human brain.

CNN Architecture

A typical CNN consists of several layers:

• Convolutional Layers: These layers apply various filters to the input image to extract features such as edges, textures, and shapes.
• Pooling Layers: These layers reduce the spatial dimensions of the feature maps, which helps to reduce the computational complexity.
• Fully Connected Layers: These layers connect every neuron in the previous layer to every neuron in the current layer.

Convolutional Layers

Convolutional layers are the core building blocks of CNNs. They apply filters to the input image to extract features. The filters are learned during the training process.

Here is an example of a convolutional layer:

<conv_layer>
    <filter> <filter> <filter>
    <filter> <filter> <filter>
    <filter> <filter> <filter>
</conv_layer>

Pooling Layers

Pooling layers reduce the spatial dimensions of the feature maps. This is done by selecting the maximum value from each region of the feature map.

There are two types of pooling layers:

• Max Pooling: Selects the maximum value from each region.
• Average Pooling: Selects the average value from each region.

Fully Connected Layers

Fully connected layers connect every neuron in the previous layer to every neuron in the current layer. This allows the network to learn complex patterns from the data.

Here is an example of a fully connected layer:

<fully_connected_layer>
    <neuron> <neuron> <neuron>
    <neuron> <neuron> <neuron>
    <neuron> <neuron> <neuron>
</fully_connected_layer>

CNN Applications

CNNs are widely used in various applications, including:

• Image Recognition: Classifying images into different categories.
• Object Detection: Detecting and classifying objects in images.
• Medical Image Analysis: Analyzing medical images for diagnosis and treatment planning.

For more information on CNN applications, please visit our CNN Applications page.