Early diagnosis of glaucoma using multi-feature analysis and DBN based classification

In this new era, the advancement towards the diagnosis of disease in its early-stage has improved. The medical field is not only equipped with the new generation devices but also the approach. The ultimate aim of this research is to diagnose a Glaucoma disorder in the optical never of the eye. Glaucoma diagnosis is a complex process as it produces no sign until it affects the eye and causes a loss of vision partially or entirely. This research provides a solution to find this disorder at the early stage by analysis the retinal parameters or features extracted using high-resolution imaging process. In this screening process, the hunt for diseases such as retinal detachment, retinopathy, retinoblastoma, and age-related molecular degeneration is processed. The classification of these diseases is complicated as many of these diseases share identical characteristics which cause the doctors to confuse in identifying the particular illness for the treatment. In this research, the multi-feature analysis methodology of classification is introduced enhanced with the machine learning algorithm and a DWT (discrete wavelet transform) to classify the diseases for the unique treatment procedures. This research shows promising results as the methodology provides more than 95 percentage of Accuracy for the Glaucoma identification.

Automated summary

In this new era, advancements in early disease diagnosis have improved, with a focus on diagnosing disorders such as Glaucoma in the eye. This research introduces a method using retinal parameters or features extracted through high-resolution imaging to identify eye diseases early, showing promising results with over 95% accuracy in Glaucoma identification.