Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

Srinivasu, Parvathaneni Naga; Balas, Valentina E.

doi:10.7717/peerj-cs.654

Cited by 33 publications

(13 citation statements)

References 51 publications

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“…An effective mechanism was designed by [ 33 ] for the brain MRI segmentation. In this mechanism, a self-learning network was utilized for the real brain MRI images.…”

Section: Related Workmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

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“…An effective mechanism was designed by [ 33 ] for the brain MRI segmentation. In this mechanism, a self-learning network was utilized for the real brain MRI images.…”

Section: Related Workmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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“…The proposed models work effectively with the structured, and it is desired to have a model that can equally perform with unstructured data. MFDA with self-learning models [33] would assist in dealing with the new disease more effectively.…”

Section: Discussionmentioning

confidence: 99%

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Swamy¹,

Praveen²,

Ahmed³

et al. 2023

Computer Systems Science and Engineering

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Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic's exponential expansion of infected COVID-19 patients has challenged the medical field's resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease's impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model's performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

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“…The proposed model has needed slightly more execution time than the other models but has exhibited a better accuracy. It is desired to have a Self-Learning for a minimal training and processing latency with better accuracy [ 78 ]. The deep learning models can address the issue of imbalanced datasets through high nonlinearity in the classification of instances.…”

Section: Discussionmentioning

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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Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

Cited by 33 publications

References 51 publications

A Precise Medical Imaging Approach for Brain MRI Image Classification

A Precise Medical Imaging Approach for Brain MRI Image Classification

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

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

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