Aged macular degeneration (AMD) leads to a progressive decline in visual acuity until reaching blindness. It is considered as an irreversible pathology where an early diagnosis remains crucial. However, the lack of ophthalmologists, the permanent increase in elderly people, and their limited mobility involves a delay in AMD diagnosis. In this paper, we propose an automated method for AMD screening. The proposed processing pipeline consists in applying the well-known Radon transform to the macula region in order to model the AMD lesions even with a moderate quality of smartphone-captured fundus images. Thereby, the relevant features are carefully selected, related to the main proprieties of drusens, and then provided to an SVM classifer. The implementation of the method into a smartphone associated to a fundus image capturing device leads to a mobile CAD system that performs higher performance AMD screening. Within this framework and to achieve a real-time implementation, an optimization approach is suggested in order to reduce the processing workload. The evaluation of our method is carried out through the three public STARE, REFUGE, and RFMiD databases. A 4-fold cross-validation approach is used to evaluate the method performance where accuracies of 100%, 95.2%, and 94.3% are respectively obtained with STARE, REFUGE, and RFMiD databases. Comparisons with the state-of-the-art methods in the literature are done. Thereafter, the robustness of the proposed method was evaluated and proved. We note that 100% accuracy was preserved despite the use of degraded quality fundus images as noisy and blurred. Moreover, the propounded method was implemented in S7-Edge and S9 Smartphone devices, where the execution times of 19 and 15 milliseconds were respectively achieved, which proves the AMD real-time detection. Taking advantage of its mobility, cost-efective, detection performance, and reduced execution time, our proposed method seems a good solution for real-time AMD screening on mobile devices.