Optimizing Image Classification Through CNNs: A Survey of Advanced Techniques
Mike Odnis, Mohammad Alshibli, Matthew Fried · 2024
We explore the latest advancements in deep learning techniques for improving the precision of image classification systems. We address the challenges of accurately categorizing photos due to factors such as background clutter, object orientations, and inconsistent illumination. Through a review of ten foundational studies, we examine state-of-the-art solutions such as data augmentation, transfer learning, and Convolutional Neural Networks (CNNs). Our objective is to identify key trends, obstacles, and areas for future research to enhance the accuracy of image classification.