Modified Convolutional Neural Network Architecture with XGBoost for Mucormycosis Detection and compare performance with XGBoost

Patients with coronavirus illness 2019, especially those in India, are more likely to see an increase in rhino-orbital mucormycosis. A well-known risk factor during COVID-19 infection and mucormycosis is diabetes mellitus (DM). With roughly 0.15 instances per 1000 people, mucormycosis is almost 82 times more common in India than it is in Western nations. Additionally, this fungus expanded quickly across numerous states, leading some of them to designate this illness an epidemic. Finding a solution for this potentially fatal fungal infection requires the aid of modern technologies, including artificial intelligence and data learning. In this paper, we combine a modified convolutional learning neural network with an XGBoost classifier to propose a unique black fungus detection method. Under the right circumstances, the CNNXGB model is made simpler by lowering the no of attributes since it is not essential to re-adjust the weight values throughout a back propagation cycle. On testing data, the mean classification performance is 98.26%, far better than current approaches. © 2023 IEEE.

Mucormycosis Detection using Hybrid Convolutional Neural Network with Support Vector Machine and Compare the performance with Support Vector Machine

Based on the patient's underlying condition, mucormycosis, often known as a black fungus illness, is an uncommon but severe disease with a high fatality rate. The large second wave of the COVID-19 epidemic has presented a challenge for the Indian healthcare system from this life-threatening powerful threat. The fungus family Mucorales causes mucormycosis, which affects numerous bodily organs. This fungal opportunistic illness spreads quickly. Recently, this unusual fungus has been infecting covid sufferers in India at greater rates than before. In India, the frequency of this black fungus illness amongst covid-19 as well as post-covid-19 patients is now on the rise. Finding a solution for this potentially fatal fungal infection requires the aid of modern technologies, including artificial intelligence and data learning. In this article, we present a unique hybrid model for black fungus identification that combines support vector machine classifier and convolutional learning network. Under the proper circumstances, the CNNSVM model is made simpler by minimizing the amount of variables because it is not important to constantly the weighting factors in a back propagation cycle. Additionally, it was shown that the SVM classifier was the best merging equivalent when the CNN was employed as a feature extractor, offering the highest accuracy-related synergy effect. On testing data, the mean classification performance was 99.3%, which is a significant improvement over current techniques. © 2023 IEEE.

A Novel Deep Learning based Black Fungus Detection using the Bagging Ensemble with K-Nearest Neighbor

Machine learning is widely employed, and broadly speaking, scientists consider applying it everywhere. Around the same period, we can see that India has been devastated by the second corona wave. In a single day, more than 4 lakh instances arrive. Meanwhile, reports of the arrival of a new, fatal fungus called Mucormycosis emerged (Black fungus). Additionally, this fungus expanded quickly throughout numerous states, leading some of them to designate this illness an epidemic. People with weak immunity functions, including those who have had the corona virus and some of whom are still recovering, are more likely to get a black fungus infection since their bodies can't successfully fight it off. Bagging Ensemble with K-Nearest Neighbor is a modified machine learning approach that will be developed in this study (BKNN). The traditional methods, including K-Nearest Neighbor ensemble with bagging classification, are the basis for the suggested methodology. After the image processing techniques, including pre-processing and segmentation, were reviewed, the accuracy score for this classifier was 96.4 percent, which would have been the highest of all the findings. This paper described how machine learning was beneficial during the Corona era, much as it would be beneficial during epidemics like mucormycosis. The last section of this essay presents accurate, graphical evidence for all items addressed, along with explicit specifications. © 2022 IEEE.

Black Fungus Classification using Adaboost with SVM-based classifier and Compare accuracy with Support Vector Machine

India recognize a severe public health issue in addition to the COVID-19 outbreak and the growing percentage of patients with related mucormycosis from 2021. An uncommon condition known as mucormycosis is brought on by fungus in the family Mucorales. Mucormycosis is a fairly uncommon illness that is caused by common environmental moulds that may be found in soil and decomposing organic materials. Spores develop into hyphae in a susceptible individual, which subsequently infect nearby tissue, including blood vessels, leading to hemorrhagic infarction. Doctors have offered many hypotheses on this. The issue is if black fungus is present in other countries given how uncontrolled it is growing in India. Patients in India with weakened immune systems are more susceptible to illnesses other than corona virus infection. The revised machine learning strategy which will be created in this work is Adaboost with an Support Vector Machine-based classifier (ASVM). Due of the difficulties in learning SVM and the differential in variety as well as efficiency over straightforward SVM classifiers, ASVM classifier is frequently believed to violate the Boosting principle. The Adaboost classifier used in the study gradually replaces SVM as the primary classifier when the weight value of the training sample changes. On testing data, the mean accuracy of the classification was 97.1%, which was much higher than that of SVM classifiers without Adaboost. © 2022 IEEE.

COVID-19 Identification and Detection from CT-Images using AI Based Ensemble Model

The corona virus infection 2019 (COVID-19) has already reached every corner of the globe, leaving many areas with insufficient access to medical supplies. When contrasted to the RT-PCR test, computed tomography (CT) images are able to provide adequate a diagnosis that is both accurate and quick about COVID-19. In this regard, the focus of this research is on the development of an AI-based prediction classifier for the identification and categorization of COVID-19. Ensembles of DL models will be used in the AIEM-DC method in order to accomplish the method's primary goal of accurate COVID-19 detection and classification. In furthermore, a pretreatment approach that relies on Gaussian filtering (GF) is used in order to get rid of clutter and increase image resolution. In addition, for the purpose of extracting the features, a shark optimization method (SOA) is used, along with an array of deep learning methods. These models include recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). In addition, for the categorization of CT images, an upgraded version of the bat method combined with a multiclass support vector machine (MSVM) architecture is used. The originality of the study is shown by the development of the prediction classifier, which includes optimized parameter tuning of the MSVM model for COVID-19 categorization. The usefulness of the AIEM-DC method was tested using a benchmark CT imaging data set, and the findings indicated the potential generalization ability of the AIEM-DC methodology in comparison to the most current state-of-the-art techniques. © 2022 IEEE.

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

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.