Brain tumor diagnosis using a step-by-step methodology based on courtship learning-based water strider algorithm

Ren, Weiguo; Bashkandi, Aysa Hasanzade; Jahanshahi, Javad Afshar; AlHamad, Ahmad; Javaheri, Danial; Mohammadi, Morteza

doi:10.1016/j.bspc.2023.104614

Cited by 18 publications

(7 citation statements)

References 30 publications

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“…Moreover, the hyperparameters of the model can be optimized through the utilization of an optimization algorithm. Ren et al (2023) , the study employed preprocessing, feature selection, and artificial neural networks for the classification of brain tumors. Furthermore, the authors utilized a specific optimization algorithm known as water strider courtship learning to optimize both the feature selection and neural network parameters.…”

Section: Related Workmentioning

confidence: 99%

A robust approach for multi-type classification of brain tumor using deep feature fusion

Chen,

Tan,

Zhang

et al. 2024

Front. Neurosci.

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Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients’ lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.

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Section: Related Workmentioning

confidence: 99%

A robust approach for multi-type classification of brain tumor using deep feature fusion

Chen,

Tan,

Zhang

et al. 2024

Front. Neurosci.

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“…The CNN is particularly adept at identifying objects, people, and scenery in images by searching for patterns in them. Medical image processing has increasingly used CNNs to detect malignancies in the breast, brain, and teeth [45]. As well as classifying image data, these algorithms are also useful for classifying other types of data, such as audio, time series, and signals [45].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…Medical image processing has increasingly used CNNs to detect malignancies in the breast, brain, and teeth [45]. As well as classifying image data, these algorithms are also useful for classifying other types of data, such as audio, time series, and signals [45]. Advanced machine learning and optimization approaches have demonstrated intriguing uses in recent medical research.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer’s severity using multitask feature extraction

Ahmadi,

Javaheri,

Khajavi

et al. 2024

PLoS ONE

Self Cite

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Alzheimer’s disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer’s disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer’s disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer’s disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

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“…DNN [31] assists to classify the features into positive or negative indication for COVID-19 disorder. Rather than other models [42] , it constitutes multiple hidden neurons connected between the “input and output layer”. In the input layer, it contains many nodes to get the input and passes to the hidden layer.…”

Section: Novel Model For Covid Cough Detection Using Optimized Deep E...mentioning

confidence: 99%

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19

Awais

Bhuva

et al. 2023

Biomedical Signal Processing and Control

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Brain tumor diagnosis using a step-by-step methodology based on courtship learning-based water strider algorithm

Cited by 18 publications

References 30 publications

A robust approach for multi-type classification of brain tumor using deep feature fusion

A robust approach for multi-type classification of brain tumor using deep feature fusion

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer’s severity using multitask feature extraction

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19

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