Abstract
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.
Highlights
In 2019, a dangerous disease known as COVID-19, caused by the novel coronavirus, spread globally, causing severe destruction of human lives and severely impacting global economic growth
System Methodologies e black fungus disease is identified via several investigation procedures such as computed tomography (CT) scans, medical resonance imaging (MRI) scans, and cell biopsy tests. All these investigation procedures are expensive, and ordinary people cannot afford these expensive tests; this study introduced a new prediction algorithm with proper integration of traditional learning algorithms such as convolutional neural network and support vector machine, which is termed as a hybrid learning-based neural network classifier (HLNNC). is algorithm follows the following principles to identify black fungus diseases, such as image acquisition, image preprocessing, extraction of image features, classification, and accuracy estimations. e aforementioned procedures were followed to predict black fungus disease based on realtime dataset images
We examine the three mentioned images (Figure 3), which pertain to a specific person, but they differ in various ways, including image color, eye orientation, backdrop color, eyeball color, and surrounding region. e most challenging aspect of interacting with images is the unpredictability of such qualities. e black fungus appears identical to the human eye; once transformed to knowledge, it could be difficult to discern a pattern among various images
Summary
In 2019, a dangerous disease known as COVID-19, caused by the novel coronavirus, spread globally, causing severe destruction of human lives and severely impacting global economic growth. During the second wave of the COVID19 disease, black fungus became a pressing issue in patients afflicted by the novel coronavirus. Because steroidoriented drugs are consumed to minimize the effect of COVID-19 infection, the immune system of the patients is Contrast Media & Molecular Imaging highly compromised, leading to the rapid spread of such fungal disease. The disease spread ratio is higher in India than in other countries. Ese patients were highly susceptible to black fungus infection, even after the second wave abated, and the national coronavirus task force declared health alert [1, 2]. Prior to the global outbreak of coronavirus, the unusual fungal disease afflicted many patients in India, with the disease frequency reported being approximately 70 to 75 times higher than that in the remainder of the world in India [3, 4]
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