Optimal deep learning based fusion model for biomedical image classification

Mansour, Romany F.; Alfar, Nada M.; Abdel‐Khalek, S.; Abdelhaq, Maha; Saeed, Rashid A.; Alsaqour, Raed

doi:10.1111/exsy.12764

Cited by 45 publications

(23 citation statements)

References 33 publications

(27 reference statements)

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“…This method has two fundamental phases: extraction of features and pattern matching or categorization. The segmentation techniques are the most crucial and difficult elements of an image feature identification system [ 5 , 6 ]. The properties of an attribute are used to extract its characteristics.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

Ajay

Nagaraj

Huang

et al. 2022

Contrast Media & Molecular Imaging

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Intelligent machines have grown in importance in recent years in object recognition in terms of their ability to envision, comprehend, and reach decisions. There are a lot of complicated algorithms that accomplish AI utilities. In addition to their use in the medical industry, these methods of object recognition have a wide range of other fields, most notably industries, in which they can be applied. In contrast to the proposed calculation, the proposed calculation is less complex and more accurate under certain SNR conditions. In the deep nervous tissue fine-tuning discriminator, phantom highlights and binding highlights are separated as sources; modified direct components are used as neuronal activation abilities; and cross entropy is used as unfortunate abilities. Optimized recognition of profound nervous tissue builds profound and periodic nervous tissue for regulatory confirmation of the corresponding signal.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

Ajay

Nagaraj

Huang

et al. 2022

Contrast Media & Molecular Imaging

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“…DTR is defined as the ratio of attacks detected to the total number of attacks. This is the best parameter to measure the performance of the model and is determined using Equation (1) [42]:…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

et al. 2022

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Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.

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“…Deep Learning techniques can manage and analyze vast biomedical image datasets, which is helpful in real-time applications. A Deep Learning-based fusion model can be used for colorectal cancer disease diagnosis and classification [ 50 ]. Predicting the in-hospital mortality caused by COVID-19 is being analyzed using an Ensemble-based Deep Neural Network [ 51 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Guleria¹,

Ahmed

Alhumam

et al. 2022

Healthcare

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Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

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Optimal deep learning based fusion model for biomedical image classification

Cited by 45 publications

References 33 publications

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

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