Aim:

        To detect and identify the Alzheimers disease detection using Deep-Learning techniques

Abstract:

      Alzheimer's disease is a neurodegenerative disorder that affects memory, thinking, and behavior. Detecting the disease early is crucial for effective management and treatment. Transfer learning is a technique where a pre-trained neural network, such as Inception V3, which was originally trained on a large dataset for image recognition tasks, is fine-tuned for a specific task, such as medical image analysis.

Introduction:

          Alzheimer's disease, a progressive neuro degenerative disorder, poses a significant global health challenge. The difficulty in diagnosing the disease in its early stages underscores the importance of advanced diagnostic tools. This article delves into the integration of transfer learning techniques with the Inception V3 model to develop a robust and accurate Alzheimer's disease detection system.

Proposed System

       The diagnosis of Alzheimers disease detection at the early stages is very important. Our proposed methodology is based on Deep Neural Network Model which trains on the Dataset and detects the image with a classification and in such image the disease gets segmented. Then we are using flask web framework to detect a Disease classification.

Advantages:

        Transfer learning, a technique in deep learning, involves leveraging pre-trained models on one task to enhance performance on a different but related task. The Inception V3 model is a prime example of a deep neural network that excels in transfer learning due to its versatile architecture and broad pre-training. Inception V3 Architecture: Inception V3 is a convolutional neural network (CNN) architecture known for its efficiency in image analysis. It features various "Inception modules," which consist of parallel convolutional operations of different sizes and pooling operations. This design enables the model to capture features at multiple scales, aiding in recognizing complex patterns within images. Pre-Training on ImageNet: Before transfer learning, Inception V3 undergoes pre-training on massive datasets, such as the ImageNet dataset. This phase imparts the model with a diverse set of features, including edges, textures, and higher-level object parts, learned from a wide range of images. Fine-Tuning for Task-Specific

Objectives:

       After pre-training, Inception V3 is fine-tuned for a specific task. Fine-tuning typically involves modifying the final layers of the network to align with the new task's objectives. For example, if the task is medical image classification, the output layer might be changed to accommodate the number of classes in the medical dataset.