Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment. However, traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level. On the other hand, models that focus on global semantic-level information might overlook critical, subtle local pathological features. To address this issue, we propose an adaptive multi-scale feature fusion network called (AMSFuse), which can adaptively combine multi-scale global and local features without compromising their individual representation. Specifically, our model incorporates global features for extracting high-level contextual information from retinal images. Concurrently, local features capture fine-grained details, such as microaneurysms, hemorrhages, and exudates, which are critical for DR diagnosis. These global and local features are adaptively fused using a fusion block, followed by an Integrated Attention Mechanism (IAM) that refines the fused features by emphasizing relevant regions, thereby enhancing classification accuracy for DR classification. Our model achieves 86.3% accuracy on the APTOS dataset and 96.6% RFMiD, both of which are comparable to state-of-the-art methods.