Deep Convolutional Neural Networks

Deep Convolutional Neural Networks, such as AlexNet, analyse visual imagery. The AlexNet architecture is much larger than previous convolutional neural networks (CNNs) used for computer vision tasks in 2012. At that time, AlexNet had 60 million parameters and 650,000 neurons, and it took almost 6 days to train on two GeForce GTX 580 (3GB) graphics processing units. Complex CNNs (ImageNet, ResNets) can run faster on GPUs containing vast datasets.

These neural networks are used to perform object recognition within scenes, cluster images by photo searching, and classify images by name.  For example, CNNs can be used to recognise faces and street signs. In medicine, they can be used to identify tumours and other visual data.  Furthermore, CNNs are leading major advances in computer vision, especially in the areas of self-driving cars, drones, robotics, cyber security, treatments for the visually impaired and medical diagnosis. 

Convolutional networks can perform optical character recognition (OCR) to digitise text.  Deep Convolutional Neural Networks are deep neural networks made up of numerous layers.  However, CNN is a neural network using convolution and pooling layers. The convolution layer combines an area or elements of input data into a smaller area to extract a feature. It is achieved by filtering an area, which is the same as multiplying weights to input data. The pooling layer selects data with the highest value within an area. These layers act to extract an important feature from the input for classification.

You are here:
home » deep convolutional neural networks

AI enhances image analysis, transforming industries with efficiency and precision
Medical Imaging Topics

Artificial Intelligence and Machine Learning: Transforming Image Analysis and Decision-Making

Artificial Intelligence and Machine Learning: Transforming Image Analysis and Decision-MakingBy | 18 minutes of reading

Artificial Intelligence and Machine Learning are revolutionising image analysis, enhancing precision, automating decisions, and driving innovation across industries such as healthcare, security, and autonomous systems.

Artificial Intelligence and Machine Learning: Transforming Image Analysis and Decision-Making Read Post »

, ,
Deep Learning Shortcutting challenges reliable predictions in medical imaging research
Editorial Review

Algorithmic Shortcutting in Medical Imaging: A Call for Rigorous Oversight in Deep Learning Applications

Algorithmic Shortcutting in Medical Imaging: A Call for Rigorous Oversight in Deep Learning ApplicationsBy | 8 minutes of reading

Algorithmic shortcutting in medical imaging highlights critical risks where deep learning models exploit confounding variables, demanding rigorous oversight and robust validation frameworks.

Algorithmic Shortcutting in Medical Imaging: A Call for Rigorous Oversight in Deep Learning Applications Read Post »

, ,
Deep learning revolutionises neuroimaging classification with efficient computational techniques
Editorial Review

Optimising Neuroimaging Classification: A Critical Review of 3D-to-2D Knowledge Distillation in Deep Learning

Optimising Neuroimaging Classification: A Critical Review of 3D-to-2D Knowledge Distillation in Deep LearningBy | 6 minutes of reading

The study critically evaluates 3D-to-2D knowledge distillation in neuroimaging classification, balancing volumetric insights with computational efficiency for real-world applications.

Optimising Neuroimaging Classification: A Critical Review of 3D-to-2D Knowledge Distillation in Deep Learning Read Post »

, ,
Scroll to Top