Automated Image Classification with CNNs and Python: A Review and Implementation

Abstract

Image recognition is a key application of computer vision and deep learning, enabling machines to automatically identify and classify objects within visual data. This paper presents a comprehensive survey of recent advancements in image recognition techniques implemented us- ing Python, with a focus on convolutional neural networks (CNNs) and variational autoencoders (VAEs). The study explores their application in real-world contexts such as healthcare, autono- mous systems, and digital forensics. Additionally, an experimental implementation is conducted using the CIFAR-10 dataset to distinguish between smartphone and tablet images. By integrating Python’s powerful libraries with deep learning models, the study demonstrates both theoretical and practical aspects of image recognition and evaluates the impact of training time on model performance. The results confirm that increasing training epochs significantly improves classifi- cation accuracy.

Received: 05 July 2025 / Accepted: 30 August 2025 / Published: 25 September 2025