Examining a Deep Learning Framework to Speed Up AI for Applications in Image Processing

Abstract— AI in many fields, especially image processing, has been greatly affected by the quick development of Deep Learning (DL) frameworks. In this work, the possibility of using deep learning approaches to improve AI efficiency when performing image-related tasks such object identification, segmentation, and classification is examined. Because of Convolutional Neural Networks' (CNNs) remarkable capacity for obtaining hierarchical features from visual data, we concentrate on them. By conducting thorough experiments and performance comparisons, this study explores the integration of two CNN-based architectures, ResNet50 and U-Net, into a unified framework tailored for image classification and segmentation tasks. The combined model leverages ResNet50's robust feature extraction for classification and U-Net's precise spatial mapping for segmentation, achieving high accuracy and computational efficiency. The proposed integrated model demonstrated superior performance compared to standalone deep learning architectures and conventional machine learning methods, establishing its viability for image processing tasks in practical applications. This approach underscores the potential of synergizing multiple CNN models to address complex image-based challenges in everyday scenarios.