Deep Learning for Image Classification: A Review

Image classification is a cornerstone of computer vision and plays a crucial role in various fields. This paper pays close attention to some traditional deep-learning approaches to image classification. Although traditional approaches, including traditional machine learning approaches, are initially practical for image classification for handcrafted feature extraction methods, they still have many limitations, such as poor scalability. These limitations limit their development. Thus, deep learning approaches have been explored, symbolizing a significant step forward in the quest for automated visual understanding. Deep learning approaches, particularly CNNs, can automatically learn and present features from raw data. They are suitable for a wide range of image classification tasks. Like any other approach, deep learning approaches have flaws, too. In addition, datasets have been instrumental in benchmarking the capabilities of algorithms, and the transfer learning approaches have positively impacted image classification models. In short, challenges have always existed, and innovation needs persistence to create a better future.