Design and Development of DenseNet Transfer Learning Model for Image Classification

Integrating deep learning with computer vision technologies and analysing their application efficiency is the main emphasis of this research. Building hierarchical neural networks, a key component of deep learning, allows for end-to-end feature learning and semantic image processing, marking a significant advancement in the field. When training deep learning algorithms, the successes in computer vision give a solid foundation. Combining these two areas has produced cutting-edge computer vision systems that outperform the state-of-the-art in object detection and machine vision image classification. This research uses a combination of everyday image classification situations to examine the deep neural network models' better performance, draw attention to their shortcomings in interpretability and generalizability, and suggest ways to enhance them in the future. In general, advancements in computer vision will be propelled by the expanding use of deep learning and its efficient integration with large amounts of visual data. This trend will pave the way for intelligent machine vision systems. After briefly introducing deep learning, this paper proposed the most recent model used for deep learning picture classification that generates an accuracy of 96.3 %. Lastly, the accuracy of our deep learning model compared with other models used in the literature for the most challenging datasets, CIFAR-10, ImageNet, and CIFAR-100.