Early Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is characterized by a gradual deterioration of memory and other cognitive functions. Early diagnosis of AD is important in order to start treatment as soon as possible and slow the progression of the disease. Deep learning-based convolutional neural network (CNN) architectures can be used to detect AD in its early stages.

The approach involves using MRI and PET scans to detect brain abnormalities associated with AD. The scans are processed and analyzed using a deep learning-based CNN architecture. The architecture consists of multiple convolutional and pooling layers, followed by fully connected layers. The network is then trained using a supervised learning approach, with labelled data from a dataset of patients with known diagnosis of AD. The trained network is then used to classify new MRI and PET scans as either AD or non-AD. The results are then used for early diagnosis of AD.

The CNN architecture is also more efficient and can process large. amounts of data quickly, making it an ideal

What is a Deep Neural Network?

 A deep neural network is an artificial intelligence system composed of multiple layers of neurons (or “nodes”) which are connected to each other and organized into a network. It is designed to learn from its environment and its previous experiences, allowing it to make predictions and decisions by processing data from its environment. Deep learning networks are used in a variety of applications, from image recognition and natural language processing to autonomous vehicles and robotics.

Building blocks of Deep Neural Network are:

 -Input layer: This layer receives input from the external environment and passes it on to the next layer. -Hidden layers: These layers contain neurons which process and modify the input data to extract features. -Output layer: This layer produces the final output of the network.

What is CNN

Convolutional neural networks (CNNs) are a type of deep learning neural network that is commonly used in computer vision. They are primarily used to classify images, identify objects, and detect features. CNNs are composed of several layers of neurons, each of which performs a specific task. The first layer is typically a convolution layer, which performs a convolution operation on the input image. This convolution operation creates feature maps from the input image, which are then processed by additional layers. The output of the CNN can be used to classify the input image.

CNN (Convolutional Neural Network) is a type of Artificial Intelligence (AI) algorithm based on a deep learning architecture. It is used for supervised and unsupervised learning tasks in image processing, natural language processing, and other areas. CNNs are also used for tasks such as object detection, image segmentation, and speech recognition.