Deep Learning Models for Image Classification Advances in Convolutional Neural Network Architectures

¹ Professor, Computer Science and Engineering, Gandhi Engineering College, Bhubaneswar, Odisha, India
² Assistant Professor, Department of Computer Science and Engineering (DS), CVR College of Engineering, Hyderabad, Telangana, India
³ Assistant Professor, Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India
⁴ Assistant Professor, Department of ECE, J.J.College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India
⁵ Professor, Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, Tamil Nadu, India
⁶ Professor, Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

prakashpathak2015@gmail.com
ballavani@gmail.com
diwakaranm@skcet.ac.in
purushothamanr@jjcet.ac.in
adlinsheeba78@gmail.com
ahila.r@newprinceshribhavani.com

Abstract

Deep learning has improved image classification tasks dramatically, where Convolutional Neural Networks (CNNs) have prevailed as the most successful architecture. But there are challenges posed by current CNN models, either due to computational expense, limited explainability, or poor generalization across domains. To overcome this, the research proposes an optimized CNN architecture that improves efficiency and scalability while enabling knowledge transfer and self-supervised learning for smaller datasets, resulting in improved accuracy. Moreover, it uses some hybrid CNN-Transformer techniques to make use of the advantages of two models and guarantee sufficient feature extraction and enhanced generalization. We will incorporate explainable AI (XAI) methods like Grad-CAM and SHAP to solve interpretability challenges, ensuring that the model is appropriate for high-stakes domains like autonomous systems and medical imaging. In addition, the outlined framework is tailored to practical applications, as it optimizes the CNN for buildings at the edge and in real time, in the speed-accuracy exchange. In designing the next generation of CNN architectures that can build upon computational efficiency, and generalization, making CNN more useful for real world applications in various fields such as health, surveillance and autonomous systems.