Basic CNN Architecture: A Detailed Explanation of the 5 Layers in Convolutional Neural Networks

A CNN architecture is designed as a structured flow of CNN layers, convolution, activation, pooling, fully connected, and output, to analyze visual information with exceptional accuracy. This architecture of CNN ensures that every stage transforms raw image pixels into rich feature representations, enabling efficient pattern discovery and class prediction.   

By progressively extracting edges, textures, shapes, and complex objects, CNN architecture in deep learning delivers superior performance in image classification, object detection, medical imaging, and other mission-critical applications.  

This guide provides a comprehensive breakdown of CNN architecture, how each layer contributes to visual understanding, the complete data processing pipeline from input to prediction, modern architectural enhancements, and real-world use cases that demonstrate why the convolutional neural network architecture remains a game-changer in deep learning innovation. 

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Overview of CNN Architecture

CNNs work differently from traditional neural network architectures. Older models connect every input to every neuron. This creates heavy computation. A CNN avoids this by scanning small regions with filters. This reduces parameters and improves speed. It also helps the model notice the same pattern even when it appears in a different part of the image.

CNNs are widely used because they understand spatial patterns. They can read medical scans, identify faces, classify images, and detect objects. These tasks depend on local details, and CNNs capture those details well.

Most CNN designs share a common set of parts:

• Filters
• Activation units
• Downsampling layers
• Dense layers
• Output functions

Each of these layers in CNN plays a specific role, contributing to effective feature extraction and accurate predictions within the CNN architecture in deep learning. Below is a CNN architecture diagram in deep learning: 

The above CNN diagram shows:

Component	Purpose
Filters	Extract edges, textures, and shapes
Activation units	Add nonlinearity to learn complex patterns
Downsampling	Reduce data size while keeping key details
Dense layers	Combine features for final decisions
Output functions	Convert scores into probabilities

CNN builds understanding step by step. The layers of CNN sharpen what the model sees. Simple features grow into meaningful patterns. This is why the CNN architecture remains a strong choice for most vision problems.

Also Read: Guide to CNN Deep Learning

5 Core Layers of CNN Architecture

A basic architecture of convolutional neural network works in five clear steps. Each layer in CNN has a simple job. Together, they turn raw images into class scores. These layers of CNN appear in almost every CNN you study, no matter how small or deep.

1. Convolution Layer

The convolution layer is the entry point of the simple CNN architecture. Instead of looking at the whole image at once, the model focuses on small blocks of pixels. This helps it notice local patterns before building a full understanding of the image. It uses multiple filters, and each one learns a different visual pattern such as an edge, corner, or texture.

How It Works: 

• A filter slides across the image one small step at a time. At each step, it multiplies its values with the pixel values under it. 
• The results are added and stored in a new grid called a feature map. This feature map shows where the learned pattern appears in the image.
• During training, the CNN adjusts the values inside each filter. Over time, the filters become good at detecting useful patterns. Early filters catch simple shapes. Deeper filters catch more complex textures and visual details.

Key actions in this layer

• Scanning small regions to understand local structure
• Extracting edges, corners, textures, and simple shapes
• Producing feature maps that highlight important visual details

Also Read: Deep Learning Tutorial for Beginners

2. Activation Layer

After the convolution layer picks up basic patterns, the model needs a way to understand more complex shapes and relationships. The activation layer makes this possible by adding nonlinearity. Without this step, an architecture of CNN in deep learning would behave like a simple linear model and would miss many details found in real images.

ReLU is the most widely used activation in convolutional neural network architecture. It keeps positive values and removes negative ones. This helps the model focus on strong signals and train faster. Sigmoid and Tanh are also used, mainly when the model needs smoother transitions between values.

How It Works

• The activation function is applied to every value in the feature map.
• If you use ReLU, negative values become zero while positive values stay as they are.
• This simple step helps the model learn curves, textures, and layered patterns.

Common activation functions

• ReLU
• Sigmoid
• Tanh

Also Read: Discover How Neural Networks Work to Transform Modern AI!

3. Pooling Layer

The pooling layer reduces the size of the feature maps created by the previous convolutional neural network layers. It selects the most important values from small regions and leaves out the rest. This keeps essential details while removing noise and extra information. The result is a lighter and faster model that still retains the core features needed for learning.

Pooling also helps the CNN stay stable when objects shift slightly within an image. A feature that appears a little to the left or right will still be captured after pooling. This makes the model more reliable and improves its ability to generalize.

How It Works

• The layer divides each feature map into small blocks.
• Depending on the pooling type, it either picks the strongest value or takes the average of the block.
• This reduces the spatial size but keeps the important signal intact.

Two common types

• Max pooling
• Average pooling

Pooling Type	What It Keeps
Max pooling	Strongest value in the region
Average pooling	Average value in the region

Also Read: Residual Networks (ResNet): Transforming Deep Learning Models

4. Fully Connected Layer

After the earlier CNN layers extract and refine features, the CNN needs a way to combine everything into a final understanding. This is where the fully connected layer comes in. The model first flattens all feature maps into one long vector. This vector represents every detail learned so far.

The flattened vector is then passed into dense units. Each unit connects to every value in the vector. These connections help the model understand how different features relate to each other. Early layers of CNN focus on edges and shapes. This layer focuses on the big picture, such as identifying whether the image shows a digit, an object, or a face.

How It Works

• Each dense unit receives all input values and multiplies them with learned weights.
• The results are added and then passed through an activation function.
• This helps the model capture high-level patterns that simpler layers cannot detect.

What this layer handles

• Combining all learned features
• Understanding high-level patterns
• Passing values to the final stage

Also Read: One-Shot Learning with Siamese Network [For Facial Recognition]

5. Output Layer

The output layer is the final step in the architecture of Convolutional Neural Network. This is where the model makes its decision. After the fully connected layer processes all features, the output layer converts those values into clear probabilities. These probabilities tell you which class the model believes the image belongs to.

For multi-class problems, the model uses Softmax. It assigns a probability to every class and ensures the values add up to one. For binary problems, the model uses Sigmoid. It produces a single probability that represents a yes or no outcome.

How It Works

• The final values from the dense units are passed into the chosen output function.
• The function transforms raw numbers into interpretable scores.
• The class with the highest score becomes the predicted label.

Role of this layer

• Producing class scores
• Returning the final prediction

Output Function	Use Case
Softmax	Many classes
Sigmoid	Yes or no task

These five layers in CNN complete the full flow of a simple CNN architecture. The model begins by detecting simple shapes and ends by delivering a clear prediction.

Also Read: Computer Vision Algorithms: Everything You Need To Know [2025]

Processing of an Image in CNN

By the end of the architecture of CNN in deep learning, the model forms a complete interpretation of what the image represents, converting learned features into final class predictions.

1. Feature Extraction

This is the first major stage of the CNN architecture.

• The convolution layer scans small regions
• Filters detect edges, corners, and textures
• The activation layer highlights strong responses
• Pooling reduces the size and keeps the key signals

These steps create feature maps. Each map focuses on something the model finds useful.

Also Read: Top Machine Learning Skills to Stand Out in 2025!

2. Building Higher-Level Patterns

Once simple features are learned, deeper layers in CNN combine them into richer patterns.

• Early layers catch thin edges
• Middle layers group edges into shapes
• Later layers form object parts or textures

Stacking these layers helps the model understand both local and global details.

3. Flattening and Preparation

After enough patterns are collected, the CNN flattens all feature maps into a single vector.
This vector becomes the input for the dense units, where the focus shifts from “what patterns exist” to “what those patterns mean.”

Also Read: Face Recognition using Machine Learning: Complete Process, Advantages & Concerns in 2025

4. Decision Stage

The dense layers of CNN learn the final relationships between features.
The output layer then converts the final values into probabilities.

Stage	Purpose
Feature extraction	Pick up edges and textures
Pattern building	Form shapes and object parts
Flattening	Prepare features for learning
Output	Produce class probabilities

5. Full Flow Example

For a 32×32 RGB input:

• Convolution learns local structure
• Activation strengthens useful signals
• Pooling reduces size
• Several cycles repeat to refine features
• Flattening creates a long vector
• Dense units connect patterns
• The output layer gives the final prediction

Also Read: Exploring the Scope of Machine Learning: Trends, Applications, and Future Opportunities

Why This Flow Works

1. Each layer of CNN reduces noise.
2. Each step adds clarity.
3. Each transformation brings the model closer to understanding the image.

This complete flow makes the CNN architecture reliable for vision tasks where tiny details and spatial patterns matter.

Performance Factors Influencing CNN Architecture

Several factors influence how well a CNN architecture performs. These factors shape how the model learns, how fast it trains, and how well it handles real-world images. Even small changes can affect accuracy and speed, so understanding these elements helps you design better models.

1. Number of Filters

More filters capture more features.
Early CNN layers may use fewer filters, while deeper layers use more to learn detailed patterns.
Too many filters can slow training, so balance matters.

Also Read: Deep Learning Algorithm [Comprehensive Guide With Examples]

2. Kernel Size

Kernel size controls how much of the image the model sees at once.
Small kernels capture fine details.
Larger kernels capture broader shapes but increase computation.

3. Model Depth

Adding more CNN layers lets the model learn richer patterns.
Deep models work well for complex tasks, but they need careful training to avoid overfitting.

4. Regularization

Regularization keeps the network stable and prevents memorizing noise.

Common methods:

• Dropout
• Early stopping
• Weight decay

Also Read: Graph Convolutional Networks: List of Applications You Need To Know

5. Hardware and Training Limits

Training deeper CNNs requires more memory and processing power.
Batch size, learning rate, and training time all depend on the available hardware.

Comparison Table

Factor	Impact
Number of filters	Feature richness
Kernel size	Detail vs. context
Depth	Complexity handling
Regularization	Generalization
Hardware	Training speed

These factors guide how a basic CNN architecture behaves during training and how well it performs on new images.

Also Read: Neural Network Architecture: Types, Components & Key Algorithms

Variations and Extensions of Basic CNN Architecture

A basic architecture gives a strong starting point, but many tasks need deeper or more efficient designs. Over the years, several variations have been introduced to improve feature learning, speed, and accuracy. 

1. Deep CNNs

Deep CNNs add more convolution and pooling layers.
More convolutional neural network layers let the model learn detailed and complex patterns.
Early layers capture edges, while deeper layers understand shapes and object parts.

This design is common in large image classification tasks.

Also Read: Handwritten Digit Recognition with CNN Using Python

2. Residual Networks

Residual networks help when models grow very deep.
They include skip connections that pass information forward without losing it.
This makes training stable and prevents the model from forgetting earlier patterns.

3. Dilated Convolutions

Dilated convolutions widen the filter’s field of view.
They help the model capture broader context without increasing parameters.
This works well for tasks like segmentation and depth estimation.

4. Depthwise Separable Convolutions

This variation breaks the convolution process into two smaller steps.
It reduces computation and keeps the model fast.
Lightweight CNNs and mobile models often use this approach.

Also Read: 16 Neural Network Project Ideas For Beginners [2025]

Comparison Table

Variation	Main Benefit
Deep CNNs	Better feature depth
Residual networks	Stable training for deep models
Dilated convolutions	Wider context understanding
Depthwise separable convolutions	Faster and lighter models

These extensions build on the core architecture and make it flexible for different needs, from mobile devices to large-scale computer vision systems.

Real-World Applications of CNN Architecture

CNNs are used across many fields because they understand patterns in visual data with high accuracy.

Application Area	How CNNs Are Used
Image classification	Classifies images into labels like animals, objects, or scenes by learning visual patterns.
Object detection	Locates multiple objects in an image and draws bounding boxes to identify each one.
Medical imaging	Helps detect tumors, fractures, and diseases by analyzing X-rays, MRIs, and CT scans.
Face recognition	Identifies and verifies faces in security systems, phones, and attendance tools.
Autonomous systems	Reads roads, signs, pedestrians, and obstacles for safe navigation.
Text extraction	Converts handwritten or printed text into digital text in OCR systems.
Quality inspection	Finds product defects on manufacturing lines by spotting texture or shape irregularities.

Conclusion

The basic architecture of Convolutional Neural Networks (CNNs) is essential for deep learning, enabling machines to process and interpret images. The five layers i.e. convolutional, pooling, activation, fully connected, and output, work in harmony to extract features and make classifications. Understanding these layers is fundamental for tasks like image classification. 

To succeed in this field, you’ll need a blend of technical expertise (neural networks, programming language, and data analytics) and soft skills (problem-solving, analytical thinking, and critical thinking).

upGrad’s machine learning courses help you learn essential skills, covering everything from neural networks to advanced CNN techniques, providing a strong foundation to build your career.

Here are the courses that can help learn CNN.

• Fundamentals of Deep Learning and Neural Networks
• Post Graduate Programme in ML & AI  from IIIT B (Executive)
• Advanced Certificate Program in Generative AI

Do you need help deciding which courses can help you in neural networking? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.