Deep Learning-Based Image Classification and Real-Time Object Detection Using CNN and YOLOv8

Abstract: Image classification is one of the fundamental areas of application of Machine Learning and Deep Learning in the field of computer vision. The exponential rise in the number of digital images produced by smartphones, cameras, satellites, and medical devices has made manual classification tedious, time-consuming, and error-prone, while conventional approaches depend on manually engineered features and give inaccurate results for complex images. This work presents a web-based Image Classification and Object Detection System that classifies Natural, Medical, and Satellite images using a trained Convolutional Neural Network and performs real-time object detection using YOLOv8. The system was trained on a multi-domain dataset of 12,783 RGB images spanning 14 classes across three groups, with all images resized to 224 × 224 pixels for the M standard input. It is deployed as a Flask web application in which users register, log in, upload an image, and receive the predicted category together with a confidence score, while every prediction is stored in an SQLite database and made available through a prediction history, categories, performance, and profile dashboard. Experimental results show confidence levels between 85.1% and 100% across the tested classes, confirming that the system performs accurate and efficient image classification with a user-friendly interface and no requirement for manual feature extraction.

Keywords: Image Classification, Deep Learning, Convolutional Neural Network, YOLOv8, Object Detection, Transfer Learning.