High blood pressure and diabetes are associated with a retinal abnormality known as Hypertensive
Retinopathy (HR). The severity-level and duration of hypertension are straightly related to the incidence
of HR-eye disease. The HR damages the pathological lesions of eyes such as arteriolar narrowing, retinal
hemorrhage, macular edema, cotton wool spots, and blood vessels. In the early stages, it is important to
detect and diagnose HR to prevent eye blindness. Currently, there are few computerize systems
developed to recognize HR. However, those systems focused on extracting features through hand-craft
and deep-learning models (DLMs) based techniques. As a result, the complex image processing
algorithms are required in case of hand-crafted features and it is difficult to define generalized features
by DLMs to recognize HR. Moreover, the classification accuracy is not up-to-the-mark even though by
using deep-feature techniques as observed in state-of-the-art HR diagnostics systems. To solve these
problems, a novel hypertensive retinopathy (DenseHyper) system is developed to detect the HR based
on a proposed trained features layer (TF-L) and dense feature transform layer (DFT-L) to the deep
residual learning (DRL) methods. The DenseHyper system consists of different multilayer dense
architecture by integrating of TF-L by convolutional neural network (CNN) to learn features from
different lesions, and generate specialized features by DFT-L. To develop DenseHyper system, a learning
based dense feature transform (DFT) approach was integrated to increase classification accuracy. Three
online sources besides one private data are gathered to test and compare the DenseHyper system. To
show the performance of the DenseHyper system, the statistical analysis is also performed on 4270
retinal fundus images through sensitivity (SE), specificity (SP), accuracy (ACC) and area under the receiver operating curve (AUC) metrics. The significant results were achieved compare to state-of-theart
methods. On average, the SE of 93%, SP of 95%, ACC of 95% and 0.96 of AUC values were obtained
through a 10-fold cross-validation test. Experimental results confirm the applicability of the DenseHyper
system to accurately diagnosis of hypertensive retinopathy.