Vision is widely acknowledged as the most critical and complex human sense, with visual data constituting roughly 90% of the information transmitted to the brain. As a result, the ability to classify image information is pivotal for enabling computers to interpret the visual world and execute tasks on our behalf. Convolutional neural networks (CNNs) have significantly advanced image classification in recent years, although their training with a vast number of parameters remains challenging, impacting recognition performance. Methods like attention mechanisms have emerged to prioritize extensive training data, which is often difficult to acquire. The limited availability of training data constitutes a significant challenge that forms the core focus of our research. Recent research has begun exploring the integration of predefined filters within CNNs to alleviate the learning burden. However, the integration of these filters, particularly with attention mechanisms, remains an ongoing area of investigation. This dissertation aims to explore effective strategies in this domain.