An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Osman, Ahmed Hamza; Aljahdali, Hani Moaiteq

doi:10.1109/access.2020.2976149

Cited by 54 publications

(21 citation statements)

References 39 publications

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“…The proposed DEL is trained several times to find the correct values of learning rate parameter and the correlation strength parameter by using an incremental training approach. Osman et al [ 25 ] stated that improvement is possible when using the ensemble boosting method. The method was integrated with a Radial Basis Function neural network algorithm and performance was increased to an accuracy of 98.4% for the WBCD dataset.…”

Section: Related Workmentioning

confidence: 99%

Breast Tumor Classification Using an Ensemble Machine Learning Method

2020

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Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD.

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Section: Related Workmentioning

confidence: 99%

Breast Tumor Classification Using an Ensemble Machine Learning Method

2020

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“…The proposed method has been achieved performance in terms of AU-ROC 88.9%. Osman et al [24] developed an effective of ensemble boosting learning approach for diagnosis of breast cancer virtual screening employing radial based function neural network models (RBFNN). They adapted 10 fold cross validation technique for best model selection and hyperparameters tuning.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Breast Cancer Through Clinical Data Using Supervised and Unsupervised Feature Selection Techniques

Haq

Saboor

et al. 2021

IEEE Access

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Breast cancer is one the most critical disease and suffered many people around the world. The efficient and correct detection of breast cancer is still needed to ensure this medical issue although the researchers around the world are proposed different diagnostic methods for detection of this disease, however these existing methods still needed further improvement to correct and efficient detection of this disease. In this study, we proposed a new breast cancer identification method by using machine learning algorithms and clinical data. In the proposed method supervised (Relief algorithm) and unsupervised (Autoencoder, PCA algorithms) techniques have been used for related features selection from data set and then these selected features have been used for training and testing of classifier support vector machine for accurate and on time detection of breast cancer. Additionally, in the proposed approach k fold cross validation method has been used for model validation and best hyperparameters selection. The model performance evaluation metrics have been used for model performance evaluation. The BC data sets have been used for testing of the proposed method. The analysis of experimental results has been demonstrated that the features selected by Relief algorithm are more related for accurate detection of Breast cancer instead of features selected by Auotencoder and PCA algorithms. The proposed method has been attained high results in terms of accuracy on selected feature selected by Relief algorithm and achieved 99.91% accuracy. We have been employed McNemar's statistical test for performance comparison of our different models. Further, the proposed method performance has been compared with baseline methods in the literature and the proposed method performance is high as compared to base line methods. Due to the high performance of the proposed method (Relief-Support vector machine) we highly recommended it for the diagnosis of breast cancer. In addition, the proposed method can be easily incorporated into the healthcare system for reliable diagnosis of Breast cancer.

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“…In addition, the study reveals a variety of AI approaches proposed to support the COVID-19 pandemic, from initial diagnosis through image diagnostics via models which help to explain COVID-19 spread and recognize new possible spread areas for the outbreak. The use of predictive diagnostic machine learning approaches has recently gained attention in the medical industry as a critical resource for clinicians [ 23 – 28 ]. Deep learning, a common field of artificial intelligence (AI), allows the creation of models end-to-end in order without requiring manual feature extraction to produce predicted results using input data.…”

Section: Related Workmentioning

confidence: 99%

“…A variety of problems such as identification of arrhythmias [ 33 ], diagnosis of skin cancer [ 34 ], identification of breast cancer [ 28 , 35 ], surgical diagnosis [ 36 ], identification of pneumonia [ 37 ], segmentation of the fundus [ 38 ] and lung segmentation [ 39 ] have been evaluated effectively by deep learning techniques. The rapid spread of the COVID-19 outbreak has demanded expertise.…”

Section: Related Workmentioning

confidence: 99%

SOM-LWL method for identification of COVID-19 on chest X-rays

et al. 2021

Self Cite

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The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.

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An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Cited by 54 publications

References 39 publications

Breast Tumor Classification Using an Ensemble Machine Learning Method

Breast Tumor Classification Using an Ensemble Machine Learning Method

Detection of Breast Cancer Through Clinical Data Using Supervised and Unsupervised Feature Selection Techniques

SOM-LWL method for identification of COVID-19 on chest X-rays

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