Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Mansour, Romany F.; Escorcia‐Gutierrez, José; Gamarra, Margarita; Díaz, Vicente García; Gupta, Deepak; Kumar, Sachin

doi:10.1007/s00521-021-06240-y

Cited by 37 publications

(18 citation statements)

References 28 publications

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“…The ELM starts with arbitrary m distinct samples with zero error, which is represented through Equation (10).…”

Section: Classification Using Elmmentioning

confidence: 99%

“…To achieve better final weights of various Artificial Neural Network (ANN) structures during the training phase, different evolutionary computing techniques have been employed [8][9][10]. The single hidden layer feedforward hidden layer network is a standard ANN structure that trained by the back-propagation (BP) learning utilizing gradient descent strategy which minimizes the cost function.…”

Section: Introductionmentioning

confidence: 99%

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On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

et al. 2021

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Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used.

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“…The ELM starts with arbitrary m distinct samples with zero error, which is represented through Equation (10).…”

Section: Classification Using Elmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

et al. 2021

Self Cite

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“…This removes the difficult task of investigating and engineering the discriminations capability of the features when enabling the reproducibility of the methods. As the development of DL approach, several studies were published using deep frameworks [5][6][7][8][9][10]. The more frequent kind of DL framework is the convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Jos�Escorcia-Gutierrez¹,

Mansour²,

Bele駉³

et al. 2022

Computers, Materials &Amp; Continua

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Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

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“…This problem has grown and has become a significant difficulty since the minority class is frequently of critical importance, as it represents favorable examples that are rare in nature or expensive to obtain [6]. This is true when considering contexts such as Big Data analytics [7,8,9,10,11,12,13], Biometrics [14,15,16,17,18,19,20,21,22], gene profiling [23], credit card fraud detection [24,25], face image retrieval [24], content-based image retrieval [26,27], disease detection [28,29,30,31,32], internet of things [33,34,35,36,37,38,39,40,41,42,43], Natural Language Processing [44,45], network security [46,47,48,49,50,51,…”

Section: Introductionmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Critical Review

Hassanat¹,

Tarawneh²,

Altarawneh³

2022

Preprint

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For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and three imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.

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Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Cited by 37 publications

References 28 publications

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Stop Oversampling for Class Imbalance Learning: A Critical Review

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