Paper Contents
Abstract
Image classification is a fundamental problem in computer vision with broad applications in fields ranging from medical imaging to the entertainment industry. In this study, we explore and compare the use of TensorFlow for constructing Convolutional Neural Network (CNN)-based models to classify images of cats and dogs. TensorFlow's versatility and accessibility via Google Colab allow for scalable deep learning applications to tackle binary classification challenges effectively. This case study provides a systematic review of methods, procedures, and challenges associated with building an image classification model using deep learning techniques. We also discuss the limitations of current models and provide insights into future improvements that can enhance accuracy and robustness (TensorFlow Documentation, 2025).
Copyright
Copyright © 2025 Snehal Shah. This is an open access article distributed under the Creative Commons Attribution License.
