One of the biggest health issues across the globe is eye diseases causing irreversible vision loss or blindness. Vision loss can be prevented by early and accurate diagnosis with proper treatment. This paper investigates three distinct models: MobileNet, ResNet, and DenseNet, along with their respective variants (MobileNet V1, V2, ResNet-50, ResNet-101, DenseNet-121, and DenseNet-169). Additionally, a pixel-wise attention mechanism is integrated with all the selected models. According to the experimental results, all the models incorporated with attention mechanism yielded good results. The findings of our study demonstrate that FocusNet based on DenseNet169 architecture with an attention mechanism, achieved the highest accuracy of 95%, followed by DenseNet 121 with 93%, MobileNet v2 with 91% ResNet 101 with 73%, and ResNet 50 lagged with accuracy of 51%. These findings highlight the effectiveness of attention mechanism with deep learning models for reliable eye disease classification. Also this study underscores the potential of attention driven deep learning framework in diagnosing ophthalmic diseases.