CNN vs ANN for Image Classification


There has been a lot of interest in creating efficient machine-learning models for picture categorization due to its growing significance in several industries, including security, autonomous driving, and healthcare. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) are two common models for classifying images. While both CNNs and ANNs can perform image classification tasks with high accuracy, their architectural designs and learning methods vary.


Identifying the elements or objects in a picture is the process of image classification. It is a key job in computer vision, having uses in anything from autonomous vehicles to the study of medical images. Deep learning has been the preferred method for classifying images in recent years, with Convolutional Neural Networks (CNNs) achieving particularly good results. For picture classification problems, we shall contrast CNNs with Artificial Neural Networks (ANNs) in this post.

A family of machine learning models known as Artificial Neural Networks (ANNs) are modeled after the structure and operation of the human brain. Information is processed and sent by numerous layers of linked nodes, or neurons, in an ANN. The input layer, which is the top layer of the network, is where the input data is sent. The output layer, which is the topmost layer of the network, creates the final result, which in the instance of picture classification is a probability distribution across all conceivable classes. Hidden layers, which are the layers between the input and output layers, carry out intermediary calculations.

ANNs can be trained using backpropagation, a technique that adjusts the weights of the connections between neurons in the network to minimize a loss function. The loss function measures the difference between the predicted output and the true output. The backpropagation algorithm computes the gradients of the loss function concerning the weights and uses them to update the weights using gradient descent.

ANNs have been used for image classification tasks, but they have some limitations. One of the main challenges of using ANNs for image classification is that they do not take into account the spatial structure of the image. ANNs treat each pixel as an independent feature, which can lead to poor performance on tasks that require spatial reasoning, such as object recognition.

To handle the spatial structure of pictures, neural networks can be classified as convolutional neural networks (CNNs). CNNs employ convolutional layers, which subject the input picture to several teachable filters. Each filter provides a feature map that indicates the locations of the features it has detected in the picture, such as an edge or texture. Following that, the feature maps are sent via a pooling layer, which downsamples them to make them smaller and the network more effective. The extracted feature maps are then subjected to further convolutional and pooling layers, which gradually extract the image's more intricate characteristics.

The final classification is carried out by one or more fully connected layers after the convolutional and pooling layers, which flatten the feature maps into a one-dimensional vector. Convolutional layers learn features, and fully connected layers create output probabilities for each class by taking into account the spatial correlations between those features.

Likewise to ANNs, backpropagation is used to train CNNs, albeit it is modified to use backpropagation across convolutional layers (backpropagation through the convolutional layers is also known as backpropagation through the layers with shared weights). Convolutional layers in backpropagation for ANNs and CNNs differ primarily in that they share weights, which allows the same filter to be applied to various parts of the input picture. As a result, the network becomes more efficient and is less likely to overfit. This decreases the number of parameters in the network.

When it comes to image classification jobs, CNNs have several benefits over ANNs. They are more adapted to capture the spatial structure of pictures, which is one of their key advantages. Convolutional layers, such as edges, lines, and forms, are used by CNNs to recognize patterns in pictures. As a result, CNNs can automatically extract pertinent characteristics from pictures, which increases their efficiency for challenging image classification tasks like locating certain items in an image.

The fact that CNNs are more effective than ANNs is another benefit. Images feature a lot of spatial redundancy, which CNNs are made to make use of. The number of parameters in the network may be drastically decreased by employing shared weights in the convolutional layers, resulting in a network that is more effective and simpler to train. Because of their increased scalability and ability to handle larger datasets and more challenging picture classification jobs, CNNs are also more efficient.

Moreover, CNNs are capable of learning hierarchical representations of the input picture, which is advantageous for transfer learning. Transfer learning is the process of starting a new task using a model that has already been trained on a sizable dataset. CNNs may be pre-trained on a large dataset, like ImageNet, and then fine-tuned on a smaller dataset for a specific image classification job since they learn hierarchical representations of the input picture. When the new dataset is tiny, this can significantly boost the model's performance.

Notwithstanding such benefits, CNNs have many drawbacks. One drawback is it requires a lot of training data to work well. This is because CNNs contain a lot of parameters and thus to prevent overfitting, they must be trained on a lot of data. They are computationally demanding, particularly for huge photos or deep structures, which is another drawback. This can make it difficult to deploy CNNs in situations with limited resources, such as mobile devices or embedded systems.

Difference Between ANN and CNN


Artificial Neural Networks (ANNs)

Convolutional Neural Networks (CNNs)


Multilayer perceptron

Convolutional layers, pooling layers, and fully connected layers

Feature extraction

Hand-crafted or learned features

Learned features through convolutional layers

Spatial information

Not specifically designed to capture spatial structure

Specifically designed to capture the spatial structure of images

Parameter sharing

No parameter sharing

Parameter sharing through convolutional layers


Less scalable due to the high number of parameters and overfitting

More scalable due to shared weights and hierarchical representations

Training data

Requires a large amount of training data to avoid overfitting

More efficient use of training data due to parameter sharing

Transfer learning

Less effective for transfer learning

Effective for transfer learning due to pre-trained models on large datasets

Computational efficiency

Less computationally efficient, especially for large images or deep architectures

More computationally efficient, especially for large images or deep architectures


Can achieve high accuracy on image classification tasks

Can achieve higher accuracy than ANNs on image classification tasks


To conclude, CNNs are typically preferred over ANNs for image classification tasks because they're designed to capture the spatial structure of photos and automatically extract relevant characteristics. Moreover, CNNs are better suited for large datasets and challenging image classification tasks than ANNs since they are more efficient and scalable. Because CNNs require a lot of training data and are computationally costly, they may not be suitable for many applications. Overall, CNNs have revolutionized the field of image classification and continue to be a focus of research.

Updated on: 13-Apr-2023

2K+ Views

Kickstart Your Career

Get certified by completing the course

Get Started