A review on neural networks approach on classifying cancers

Mahmood, Maha; Al‐Khateeb, Belal; Alwash, Wisam Makki

doi:10.11591/ijai.v9.i2.pp317-326

Cited by 19 publications

(17 citation statements)

References 0 publications

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“…Artificial neural networks (ANN) is a mathematical processing method that can be used for both classification and regression [13,14]. The neurons make it a powerful learning model for this reason for Int J Elec & Comp Eng ISSN: 2088-8708 …”

Section: Background Of the Study 21 Sparkmentioning

confidence: 99%

A smart method for spark using neural network for big data

Rahman

Hossen

Sultana

et al. 2021

IJECE

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Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of distributed memory to process big data. As the performance of the spark is mostly being affected by the spark predominant configuration parameters, it is challenging to achieve the optimal result from spark. The current practice of tuning the parameters is ineffective, as it is performed manually. Manual tuning is challenging for large space of parameters and complex interactions with and among the parameters. This paper proposes a more effective, self-tuning approach subject to a neural network called Smart method for spark using neural network for big data (SSNNB) to avoid the disadvantages of manual tuning of the parameters. The paper has selected five predominant parameters with five different sizes of data to test the approach. The proposed approach has increased the speed of around 30% compared with the default parameter configuration.

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Section: Background Of the Study 21 Sparkmentioning

confidence: 99%

A smart method for spark using neural network for big data

Rahman

Hossen

Sultana

et al. 2021

IJECE

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“…ANN is widely used to make predictions or predictions. Different researchers have examined the use of ANN with back-propagation training algorithms to predict cancer cases [4]. ANN with the type of feed-forward network or back-propagation used in this study has been shown to provide good results and results for prediction purposes [3].…”

Section: Neural Networkmentioning

confidence: 99%

“…For non-regulatory networks, it is a learning network by collecting all the same input patterns in one group, and no targets are given. Examples of non-regulatory networks are the Konohen network and the adaptive resonance theory [4]. In this research, the propagator network algorithm is chosen because the propagator network's goal is to balance the ability to respond to input patterns during learning and respond to other input patterns that are very similar to the patterns used in learning [5].…”

Section: Neural Networkmentioning

confidence: 99%

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Classification of Breast Cancer Patients Using Neural Network Technique

Badarudin¹,

Ghazali²,

Alahdal³

et al. 2021

JSCDM

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This work develops an Artificial Neural Network (ANN) model for performing Breast Cancer (BC) classification tasks. The design of the model considers studying different ANN architectures from the literature and chooses the one with the best performance. This ANN model aims to classify BC cases more systematically and more quickly. It provides facilities in the field of medicine to detect breast cancer among women. The ANN classification model is able to achieve an average accuracy of 98.88 % with an average run time of 0.182 seconds. Using this model, the classification of BC can be carried out much more faster than manual diagnosis and with good enough accuracy.

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“…The model was used for classifying the abnormalities present in the sickle cell anemia disease to give a better insight into managing the concerned patient's life and it achieved a high classification prediction accuracy of 95.92% [5]. Neural networks were applied to cancer disease to classify lymph, neck and head, and breast cancer that might help clinicians and oncologists in the prediction and prognosis of cancer [6]. For heart disease, machine learning techniques can be useful to predict risk at an early stage.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an incremental deep learning model for survival prediction of cardiovascular patients

Elyassami

Kaddour²

2021

IJ-AI

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<span lang="EN-US">Cardiovascular diseases remain the leading cause of death, taking an estimated 17.9 million lives each year and representing 31% of all global deaths. The patient records including blood reports, cardiac echo reports, and physician’s notes can be used to perform feature analysis and to accurately classify heart disease patients. In this paper, an incremental deep learning model was developed and trained with stochastic gradient descent using feedforward neural networks. The chi-square test and the dropout regularization have been incorporated into the model to improve the generalization capabilities and the performance of the heart disease patients' classification model. The impact of the learning rate and the depth of neural networks on the performance were explored. The hyperbolic tangent, the rectifier linear unit, the Maxout, and the exponential rectifier linear unit were used as activation functions for the hidden and the output layer neurons. To avoid over-optimistic results, the performance of the proposed model was evaluated using balanced accuracy and the overall predictive value in addition to the accuracy, sensitivity, and specificity. The obtained results are promising, and the proposed model can be applied to a larger dataset and used by physicians to accurately classify heart disease patients.</span>

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A review on neural networks approach on classifying cancers

Cited by 19 publications

References 0 publications

A smart method for spark using neural network for big data

A smart method for spark using neural network for big data

Classification of Breast Cancer Patients Using Neural Network Technique

Implementation of an incremental deep learning model for survival prediction of cardiovascular patients

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