Leveraging Convolutional Neural Network for Image Classification Using Machine Learning
Author(s)
Abstract
Image classification is one of the most important tasks in computer vision and machine learning, where images are categorized into predefined classes based on their visual content. Traditional image classification methods require manual feature extraction, which can reduce efficiency and accuracy when dealing with large datasets. Convolutional Neural Networks (CNNs), a deep learning approach, have significantly improved image classification performance by automatically extracting hierarchical features from images. This paper presents a CNN-based image classification system using machine learning techniques to enhance accuracy and efficiency. The proposed system includes image preprocessing, feature extraction, model training, and classification. Experimental analysis shows that CNN-based models outperform traditional machine learning methods in terms of accuracy, robustness, and scalability. The system can be applied in various fields such as healthcare, surveillance, agriculture, autonomous vehicles, and object recognition.
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