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Cataracts are lenticular opacities that can appear in different parts of the lens in the eye and are a leading cause of blindness globally. Accurate detection and early‐stage diagnosis can prevent the cataract and improve the quality of life for cataract patients. However, clinical cataract detection and grading require the expertise of trained eye specialists, which may impede everyone's early intervention due to the underlying expenses. This article proposed a computer aid diagnosis method for cataract detection, which also grades the severity of cataracts from fundus retinal images such as normal, mild, moderate, and severe. The proposed method uses a hybrid approach in which various pre‐trained convolutional neural networks (AlexNet, VGGNet, ResNet) with transfer learning are used to extract features. These feature vectors of each network individually and in the fused form are applied on the support vector machine classifiers for 4‐stage cataract classification. This architecture also takes advantage of ensemble learning by applying a majority voting scheme on the predictions of these SVM classifiers. The fundus cataract images are obtained from several open‐access datasets and arranged into 4‐classes with the assistance of an eye specialist. Since all the collected images are not suitable for diagnosis, an image quality selection module is included with this method to determine the quality of fundus images. The proposed method achieved 96.25% 4‐class classification accuracy. According to the experimental results, the proposed method is effective for cataract classification and outperforms conventional methods. [ABSTRACT FROM AUTHOR]