IMPROVING GOOGLENET FOR HYPERTENSION CLASSIFICATION BASED ON RETINAL VESSEL IMAGES

Abstract

Retinal vessel images contain a wide range of information that is a manifestation of the disease. Features of these images extracted from learning models need to be improved and optimized to help the disease classification process be more accurate. The higher the classification ability, the easier the diagnosis and treatment of the disease. The challenge is to extract deep features on the basis of retinal images with initial parameters consistent with the corresponding morphology. In this paper, we propose an improvement process of GoogLeNet based on the waveform morphology mechanism to classify hypertension on retinal images. The proposed method includes two stages: preprocessing of input parameters for the learning model with waveform morphology, GoogLeNet feature extraction with improvements in the number of Inception layers supporting deep feature extraction. The results were tested on the STARE dataset with an accuracy of 93.25 %. The classification results are compared with some recent methods and show more positive results.