Exploration of Morphological Procedure on the Recognition of Fundus Image

Shifani, S. Agnes; Ramkumar, G.; Nanammal, V.; Prabu, R

doi:10.1166/jctn.2020.8841

Cited by 6 publications

(2 citation statements)

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“…The major advantage identified from this paper is the application of K-Means Optimization Logic to optimize the digital glaucoma images with high contrast to estimate the affected regions easily with respect to the proposed classification logic [ 16 ]. The image-based changes recognition is identified by using classical morphological procedures [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies

Jesi

Aslam

Ramkumar

et al. 2021

Contrast Media & Molecular Imaging

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Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one’s eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.

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Section: Related Workmentioning

confidence: 99%

Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies

Jesi

Aslam

Ramkumar

et al. 2021

Contrast Media & Molecular Imaging

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“…The state of Maharashtra (Western), which includes Mumbai City, has documented approximately 2000 black fungus cases and eight deaths due to mucormycosis. The purpose of this study was to examine the black fungus disease, its characteristics, and the method of treatment associated with mucormycosis in coronavirus patients [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning‐Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology

Karthikeyan

Ramkumar

Sekar³

et al. 2022

Contrast Media & Molecular Imaging

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Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues due to this disease, which has resulted in several fatalities. The first COVID-19 wave caused devastation across the world owing to its virulence and led to a massive loss in human lives, impacting the country’s economy drastically. A dangerous disease called mucormycosis was discovered worldwide during the second COVID-19 wave, in 2021, which lasted from April to July. The mucormycosis disease is commonly known as “black fungus,” which belongs to the fungus family Mucorales. It is usually a rare disease, but the level of destruction caused by the disease is vast and unpredictable. This disease mainly targets people already suffering from other diseases and consuming heavy medication to counter the disease they are suffering from. This is because of the reduction in antibodies in the affected people. Therefore, the patient’s body does not have the ability to act against fungus-oriented infections. This black fungus is more commonly identified in patients with coronavirus disease in certain country. The condition frequently manifests on skin, but it can also harm organs such as eyes and brain. This study intends to design a modified neural network logic for an artificial intelligence (AI) strategy with learning principles, called a hybrid learning-based neural network classifier (HLNNC). The proposed method is based on well-known techniques such as convolutional neural network (CNN) and support vector machine (SVM). This article discusses a dataset containing several eye photographs of patients with and without black fungus infection. These images were collected from the real-time records of people afflicted with COVID followed by the black fungus. This proposed HLNNC scheme identifies the black fungus disease based on the following image processing procedures: image acquisition, preprocessing, feature extraction, and classification; these procedures were performed considering the dataset training and testing principles with proper performance analysis. The results of the procedure are provided in a graphical format with the precise specification, and the efficacy of the proposed method is established.

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