What is Convolutional Neural Network?

Convolutional Neural Networks (CNN) is a form of Artificial Neural Network used largely for image identification and processing. It is a powerful tool that can recognize patterns in images but requires millions of labeled data points for training. Here in this article we will deep dive and explore the CNNs in depth.

Table of Contents:

• What is CNN?
• How do convolutional neural networks work?
• Convolutional Neural Network Architecture
• Convolutional Neural Network Layers
• Important aspects of CNN
• Training a Convolutional Neural Network
• Evaluating CNN Model Performance
• Implementation of Convolutional Neural Networks using TensorFlow and Keras
• Image Processing in CNN
• Limitations of CNN
• CNNs in Computer Vision
• Best Practices for Using CNN in Deep Learning
• Conclusion

What is CNN?

It was first introduced by Yann LeCun. It was also called ConvNets, in the 1980s. A Convolutional Neural Network (CNN) is a form of Artificial Neural Network used largely for image identification and processing. It is a powerful tool that can recognize patterns in images but requires millions of labeled data points for training. Even though CNNs were created to handle issues with visual imaging, they may also be used for image categorization, natural language processing, drug development, and health risk assessments. It can also assist self-driving automobiles with depth estimates.

How Do Convolutional Neural Networks Work?

The higher performance of Convolutional Neural Networks with pictures, voice, or audio signal inputs sets them apart from conventional neural networks. As  we mentioned earlier, it is divided into three sorts of layers:

• Convolution Layer
• Pooling Layer
• Fully-Connected Layer

We will further discuss these layers in detail in this blog.  If we give an input image, it goes to convolution+Relu, each area has a 3D, RGB, then it goes to the next pooling layer where it shrinks the max value and this cycle keeps repeating. This is the learning process. We try to classify the values and then we have to apply neural nets and try to figure out what the actual image is. Given that it is a car, softmax gives a value of 0 to 1, the probability of the maximum is identified as the car.

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Convolutional Neural Network Architecture

The CNN architecture is made up of two important components:

• In a process known as Feature Extraction, a convolution tool isolates and identifies the distinct characteristics of a picture for analysis. This feature extraction consists of an input, convolution layer, and pooling layer. 
• Another component present in CNN architecture is classification in which we have fully connected the layer and output. The classification component is a fully connected layer that uses the output of the convolution process to forecast the image’s class using the information acquired in earlier stages.

CNN becomes more complex with each increasing layer. This is done to detect larger areas of the picture. The first few layers mainly concentrate on basic elements like colors and borders. As the images travel through the CNN layers, it starts to differentiate the bigger components or features of the images, and eventually, identifies the target object. We will talk about these layers in detail in the upcoming section.

Convolutional Neural Network Layers

Convolutional layers, pooling layers, and fully-connected (FC) layers are the three types of layers that make up the CNN. A CNN architecture will be constructed when these layers are layered. Here is a detailed explanation of these three layers. 

1. Convolution layer

The convolutional layer is the most essential component of the CNN as this is where most processing takes place. It needs input data, a filter, and a feature map, among other things. Let’s pretend the input is a color picture, which is made up of a 3D matrix of pixels. This implies the input will have three dimensions: height, width, and depth, which match the RGB color space of a picture. Here we try to decompose RGB to a multidimensional layer and apply a filter to each layer.  A feature detector, commonly known as  kernel or filter, traverses over the image’s receptive fields, checking for the presence of each feature

2. Pooling Layer

Pooling layers is a dimension-reduction technique that reduces the number of input parameters. The pooling process sweeps a filter across the input just like the convolutional layer. However, this filter does not contain any weights, unlike the convolution layer.  Instead, the kernel uses an aggregation function to populate the output array from the values in the receptive field. The pooling layer is also known as the Downsampling process. And, maximum pooling and average pooling are the two basic forms of pooling.

3. Fully-Connected Layer

The fully-connected layer’s name is a perfect description of what it is. As previously stated, with partly connected layers, the input image’s pixel values are not directly connected to the output layer.   However, each node in the output layer links directly to a node in the preceding layer in the fully connected layer. This layer conducts categorization based on the characteristics retrieved by the preceding layers and the filters applied to them While convolutional and pooling layers generally utilize ReLu functions to distinguish inputs, FC layers typically use a softmax activation function to provide a probability sequence ranging between 0 to 1.

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Important aspects of CNN

The important aspects of CNN are filters, receptive field, stride, and padding. 

1. Filters

Filters in Convolutional Neural Networks recognize spatial patterns such as edges in an image by detecting changes in the picture’s intensity values.

2. Receptive Field

Receptive fields are specified areas of space or spatial constructs that include units that offer input to a layer’s collection of units. The filter size of a layer within a Convolution Neural Network determines the receptive field.

3. Stride

The kernel’s stride is the number of pixels it traverses across the input matrix. Although stride values of two or more are uncommon, a bigger stride results in a lesser output.

4. Padding

Padding essentially increases the number of images that a convolutional neural network can handle. Each pixel is scanned by the kernel/filter as it goes over the picture, converting the image into a smaller image.

Training a Convolutional Neural Network

Before attempting to train a CNN model, you should know that it involves feeding it a labeled dataset and allowing it to adjust its internal weights to minimize the prediction error. You will use optimization algorithms like Stochastic Gradient Descent (SGD)  or Adam, and particularly a loss function like cross-entropy for classification. The model implements backpropagation to update its filters and weights based on the loss. CNN gets better at detecting relevant features that help it distinguish between classes. This happens after you iterate over epochs. The dataset needs to be sufficiently large and varied to ensure generalization.

Evaluating CNN Model Performance

The next crucial step after successfully training your Convolutional Neural Network (CNN) is to improve its performance. This will assist you in measuring how well your model can notice, uncover data, and point out any areas that need improvement. For this, you would need a classification report.

Accuracy

Accuracy is the most basic yet widely used performance metric, especially in classification tasks. It is defined as the ratio of correctly predicted labels to the total number of predictions.

Formula:

Accuracy = (Correct Predictions / Total Predictions) × 100

Example in PyTorch:

Explanation: Here, when one class dominates, it can be misleading in imbalanced datasets, even though accuracy is easy to interpret. That’s why additional metrics are essential.

Confusion Matrix

A confusion matrix gives a more detailed snapshot of your CNN’s performance. It tells you not just how many predictions were right, but what types of errors the model is making.

Matrix:

Implementation (Scikit-learn):

Explanation: Here, this matrix helps identify patterns of misclassification, guiding further fine-tuning of your model architecture or training process.

Precision, Recall, and F1 Score: Granular Evaluation

Precision:

Tells you how many of the positive predictions made by your CNN were correct.

Formula:

Precision = TP / (TP + FP)

Recall:

Measures how many of the actual positives your CNN was able to capture.

Formula:

Recall = TP / (TP + FN)

F1 Score:

The harmonic mean of precision and recall. It balances the trade-off between the two.

Formula:

F1 Score = 2 × (Precision × Recall) / (Precision + Recall)

These metrics are especially valuable when dealing with imbalanced datasets where certain classes have significantly more data than others (e.g, detecting rare diseases in medical imaging).

Cross-Validation for CNNs: Ensuring Generalization

In your traditional machine learning, utilizing K-fold cross-validation was common. Due to the high computational cost of training CNNs, hold-out validation or stratified train/validation/test splits are typically used. Cross-validation can be adapted by training the CNN multiple times on different folds and averaging the performance metrics.

Implement this Pytorch tip in your code:

Use torch.utils.data.random_split() or sklearn.model_selection.train_test_split() to create robust data splits.

Loss Curve Analysis

Understanding learning behavior by plotting the training loss vs. validation loss over epochs helps detect problems like:

• • Overfitting (training loss decreases, validation loss increases)

• • Underfitting (both losses remain high)

• • Optimal learning (both losses decrease and converge)

Example using Matplotlib:

Explanation: Here, monitoring these curves allows you to adjust hyperparameters such as learning rate, number of epochs, or regularization techniques like dropout and weight decay.

ROC-AUC Score

For binary classification problems, the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) are invaluable. They measure the model’s ability to distinguish between classes based on probability thresholds.

Implementation code:

Explanation: Here, a score of 1.0 means perfect classification; 0.5 means random guessing.

Top-K Accuracy (For Multi-Class Models)

In complex classification tasks, Top-K Accuracy evaluates whether the true label is within the top K predicted probabilities.

Code snippet:

Implementation of Convolutional Neural Networks using TensorFlow and Keras

1. Importing the necessary libraries

import numpy as np
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

3. Normalizing the data

x_train, x_train = x_train / 255.0, x_test / 255.0

4. Converting into labels

y_train = to_categorical(y_train, 10)

y_test = to_categorical(y_test, 10)

5. Building the CNN Model

model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu')
])

6. Compiling the model

model.compile(optimizer='adam', 
loss='categorical_crossentropy', 
metrics=['accuracy'])

7. Training the model

history = model.fit(x_train, 
y_train, 
epochs=10, 
validation_data=(x_test, y_test), 
batch_size=64)

9. Evaluating the model

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print(f'nTest accuracy: {test_acc}')

Image Processing in CNN

In the following section, we will take an image and implement CNN. We will perform basic image processing using TensorFlow by applying convolution, ReLU activation, and max pooling to enhance and simplify a grayscale image.

Remember to name the image exactly as written in the code, check the extension of the image, and install all dependencies and frameworks like numpy, tensorflow, and matplotlib.

Use this code to check if the image is displaying:

Output:

Code example to process an image:

Output: 

Explanation: Here, the TensorFlow-based code demonstrates basic image processing using convolution, activation, and pooling operations. It begins by loading a grayscale version of an image (ribbon.jpg), resizing it, and displaying it. A sharpening kernel is defined and applied to the image using a convolution layer to enhance edges. The result is then passed through a ReLU activation function to highlight positive features and suppress negative values. Finally, max pooling is applied to reduce the image dimensions while retaining important features. The code concludes by visually displaying the outputs of all three steps: convolution, activation, and pooling.

Applications of CNNs span across:

CNNs are the engine behind many modern visual AI systems.

• • Medical Imaging (detecting tumors)

• • Self-Driving Cars (lane detection, object detection)

• • Security (facial recognition)

• • Agriculture (disease detection in crops)

• • Industrial QA (defect detection)

Limitations of CNN

• Because of operations like max pool, a Convolutional Neural Network is substantially slower.
• If the CNN contains several layers, the training process will take a long time if the machine does not have a powerful GPU.
• To analyze and train the neural network, a ConvNet requires a huge dataset.
• It fails when it comes to comprehending the contents of a picture.

CNNs in Computer Vision

CNNs are the foundation of modern computer vision. They power systems in autonomous vehicles, facial recognition apps, augmented reality, and even quality control in factories. CNNs can detect objects, segment images, classify scenes, and track motion. Their ability to learn hierarchical features makes them ideal for complex visual tasks.

Best Practices for Using CNN in Deep Learning

Following these practices will help you build reliable CNN models.

• • Start Simple: Begin with basic architectures like LeNet before jumping into complex ones.

• • Use Pretrained Models: Leverage transfer learning to reduce training time.

• • Apply Data Augmentation: Increases dataset diversity without new data.

• • Regularize Properly: Use dropout and batch normalization to combat overfitting.

• • Monitor Training: Use validation curves and early stopping.

What is Convolutional Neural Network – FAQS