Mohamed Akil

TL;DR: In this article, a new U-form DL architecture using lightweight convolution blocks was proposed to preserve a higher segmentation performance while reducing the computational complexity, which can achieve a better trade-off between the retinal blood vessel detection rate and the detection time with average accuracy of 0.978 and 0.98 in 0.59 s and0.48 s, respectively for DRIVE and STARE database fundus images.

TL;DR: An extension of the AAA rapid prototyping methodology for the optimized implementation of real-time applications onto reconfigurable circuits is presented, based on an unified model of factorized data dependence graphs as well to specify the application algorihtm, as to deduce the possible implementations onto reconfigured hardware.

TL;DR: This chapter presents an overview of the used CNN Deep Learning-based methods in detection of retinal abnormalities related to the most severe ocular diseases in retinal images, where network architectures, post/preprocessing and evaluation experiments are detailed.

TL;DR: NeoVascularization occurs in the Proliferative Diabetic Retinopathy stage, where the development progress of new vessels presents a high risk for severe vision loss and blindness, and early NV detection is primordial to preserve patient's vision.

TL;DR: The proposed smartphone app provides a cost-effective and widely accessible mobile platform for early screening of glaucoma in remote clinics or areas with limited access to fundus cameras and ophthalmologists.