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@article{200661,
        author = {Rutvi M. Baraiya and Bhoomika B. Chauhan},
        title = {Comprehensive Analysis of Loopholes and Solutions in Convolutional Neural Networks: Addressing Advanced Challenges with Attention Mechanisms and Interpretability Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2145-2153},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200661},
        abstract = {Convolutional Neural Networks (CNNs) have achieved remarkable success in computer vision tasks, yet they face critical challenges such as vulnerability to adversarial attacks, limited interpretability, and poor contextual awareness. This paper presents a focused analysis of these loopholes and explores solutions through the integration of attention mechanisms and interpretability techniques. By incorporating self-attention and spatial-channel attention modules, CNNs can better capture salient features and context. Additionally, methods like Grad-CAM and LIME enhance model transparency. Experimental results demonstrate improved performance, robustness, and explain ability, paving the way for more reliable and interpretable CNN-based systems[1].},
        keywords = {Comprehensive analysis, convolutional neural networks, CNN loopholes, advanced challenges, attention mechanisms, interpretability, deep learning, model transparency, neural network limitations, explainable AI, feature visualization, saliency maps, model robustness, network architecture, performance improvement, attention-based models, neural network interpretability, CNN optimization, explainability techniques, deep learning interpretability.},
        month = {May},
        }