System and Method for Deep Learning-Based Image Recognition

Abstract

The present invention provides a system and method for deep learning-based image recognition that utilizes advanced neural network architectures, particularly convolutional neural networks (CNNs), to classify objects and patterns within digital images. The system includes modules for image acquisition, preprocessing, feature extraction, classification, and decision-making, enabling real-time, high-accuracy recognition across diverse applications such as healthcare, autonomous vehicles, and security surveillance. By leveraging deep learning models, the system enhances classification accuracy, scalability, and adaptability, while maintaining computational efficiency for deployment in both resource-constrained environments and large-scale platforms.

Specification

Description:The present invention relates to the field of artificial intelligence and image processing, specifically focusing on a system and method for deep learning-based image recognition. More particularly, it pertains to the use of deep learning models, such as convolutional neural networks (CNNs), for efficiently recognizing, classifying, and interpreting patterns or objects within digital images, with applications across diverse industries, including healthcare, autonomous vehicles, security, and manufacturing.
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.

Image recognition has been an essential technology , Claims:1. A system for deep learning-based image recognition, comprising:
o An image acquisition module configured to receive an image;
o A preprocessing module configured to preprocess the image by resizing, normalizing, and enhancing the image;
o A deep learning-based recognition engine comprising a trained neural network model, the recognition engine configured to classify the preprocessed image into one or more predefined categories based on the extracted features; and
o A decision-making module configured to output a classification result including one or more labels and confidence scores.
2. A method for deep learning-based image recognition, comprising:
o Receiving an image;
o Preprocessing the image by resizing, normalizing, and enhancing the image;
o Extracting features from the preprocessed image using a convolutional neural network (CNN);
o Classifying the features into one or more predefined categories using the trained neural network model; and
o Outputting a classification result including one or more

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