NEURAL NETWORK MODEL FOR IMAGE RECOGNITION

Abstract

The present invention relates to a neural network model for image recognition that combines convolutional, recurrent, and attention mechanisms to enhance classification accuracy, computational efficiency, and interpretability. The model begins by preprocessing the input image, followed by convolutional layers for feature extraction, recurrent layers for capturing spatial dependencies, and an attention mechanism for focusing on relevant image regions. The final output is generated through fully connected layers with a softmax activation function, providing a classification probability distribution over predefined categories. This hybrid architecture improves performance in image recognition tasks across various applications, including object classification, medical imaging, and real-time surveillance, while also enabling efficient use of computational resources.

Specification

Description:The embodiments of the present invention generally relates generally to the field of machine learning and artificial intelligence, and more specifically to a neural network model for image recognition. The invention involves a hybrid architecture that combines convolutional layers for feature extraction, recurrent layers for capturing spatial dependencies, and attention mechanisms for enhancing the focus on key regions of an image, thereby improving the accuracy, efficiency, and interpretability of image recognition tasks across a variety of applications.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

, Claims:1. A neural network model for image recognition, comprising:
an input layer configured to receive an input image;
one or more convolutional layers for extracting features from the input image;
at least one recurrent layer configured to capture spatial dependencies between the extracted features;
an attention mechanism configured to focus on important regions of the input image;
one or more fully connected layers for aggregating the extracted features and generating output;
a softmax output layer providing a classification probability distribution over predefined categories.

2. The neural network model of claim 1, wherein the recurrent layer is a Long Short-Term Memory (LSTM) network.

3. The neural network model of claim 1, wherein the recurrent layer is a Gated Recurrent Unit (GRU) network.

4. The neural network model of claim 1, wherein the convolutional layers include batch normalization and activation functions to enhance model training stability.

5. The neural network model of claim 1, wherein the att

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