<![CDATA[Overfitting remains a critical challenge in developing deep learning–based image classification models, particularly as modern architectures become increasingly complex and parameter-intensive. Although convolutional and transformer-based models have demonstrated strong predictive performance, their generalization capability depends strongly on the availability of large, well-annotated, and diverse training datasets. This condition is often difficult to achieve in real-world domains such as medicine, agriculture, and industrial inspection. Previous survey studies have examined various techniques related to image classification, data augmentation, and optimization strategies; however, these studies typically analyse individual approaches in isolation. As a result, opportunities remain to further synthesise the relationships among the underlying causes of overfitting, mitigation strategies, and training parameter configurations within a unified analytical perspective. This study employed the PRISMA framework to conduct a Systematic Literature Review (SLR). A total of 174 primary studies published between 2020 and 2025 were traced to address the gap. The review identifies three major sources of overfitting in image classification tasks: limited labeled data, model architecture complexity, and data and label quality issues. Based on these findings, the study synthesises the corresponding mitigation strategies reported in the literature. The main contribution of this study is not the proposal of a new theoretical taxonomy, but the systematic organisation and synthesis of methodological evidence into an integrated analytical framework. The resulting analytical framework relates the underlying causes of overfitting, mitigation strategies, training optimisation mechanisms, and parameter configuration practices within a unified perspective, thereby facilitating a more integrated interpretation of how these complementary aspects contribute to model generalisation in deep learning–based image classification.]]>
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