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Medical image analysis for complex tasks such as severity grading and disease subtype classification poses significant challenges due to intricate and similar visual patterns among classes, scarcity of labeled data, and variability in expert interpretations. Although deep learning models can capture complex visual patterns for medical image classification, many architectures still struggle to distinguish subtle classes because they do not adequately capture inter-class similarity and intra-class variability, which can lead to incorrect diagnoses. To address this, we propose the AGGRNet framework to extract informative and non-informative features to effectively understand fine-grained visual patterns and improve classification for complex medical image analysis tasks. Experimental results show that our model achieves state-of-the-art performance on 5 distinct medical imaging datasets, with the maximum improvement of 5% over SOTA models. The code is available at: https://github.com/AnshMakwe/AGGRNet-Feature-Extraction-and-Aggregation