A Novel Learning Algorithm of Back-Propagation Neural Network

Gong, Bing

doi:10.1109/case.2009.146

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…After several iterations of the training and validation phases, the model performance is tested on the test set. Many machine learning algorithms exist, namely, SVM, KNNs, ANNs [ 33 ], feed-forward neural networks [ 34 ], and BPNNs [ 35 ], all of which have proven their superior ability to classify biomedical images, such as the MRI images of brain tumours.…”

Section: Overviewmentioning

confidence: 99%

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

Senan

Jadhav

Rassem

et al. 2022

Computational and Mathematical Methods in Medicine

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Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients’ chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.

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Section: Overviewmentioning

confidence: 99%

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

Senan

Jadhav

Rassem

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Many studies used the adaptive training rate by adopting the monotonicity function such as [7] - [18] used the exponential to increase the speed of the BP algorithm. Based on the discussion above, the new formula of the training rate is proposed as follows.…”

Section: Himentioning

confidence: 99%

“…Many recent studies have attempted to solve the slow training required for the SBP algorithm through adaptation of parameters such as the training rate, which is controlled by the weight adjustment along with the descent direction [6]. Gong [7] proposed a novel algorithm of the neural network NBPNN based on a self-adaptive learning factor. Those algorithms were tested on XOR 2-bit or two dimensionally.…”

Section: Introductionmentioning

confidence: 99%

Dynamic training rate for backpropagation learning algorithm

Al_Duais

Yaakub

Yusoff

2013

2013 IEEE 11th Malaysia International Conference on Communications (MICC)

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In this paper, we created a dynamic function training rate for the Back propagation learning algorithm to avoid the local minimum and to speed up training. The Back propagation with dynamic training rate (BPDR) algorithm uses the sigmoid function. The 2-dimensional XOR problem and iris data were used as benchmarks to test the effects of the dynamic training rate formulated in this paper. The results of these experiments demonstrate that the BPDR algorithm is advantageous with regards to both generalization performance and training speed. The stop training or limited error was determined by1.0

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“…The back propagation algorithm for the Iris data problem uses the generalized delta rule including the momentum term to ensure better performance [13], [14], [15].…”

Section: Neural Back Propagationmentioning

confidence: 99%

Performance Enhancement of Data Classification using Selectively Cloned Genetic Algorithm for Neural Network

Kaur¹,

Nelapati²

2010

IJCIS

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The paper demonstrates performance enhancement using selective cloning on evolutionary neural network over the conventional genetic algorithm and neural back propagation algorithm for data classification. Introduction of selective cloning improves the convergence rate of the genetic algorithm without compromising on the classification errors. The selective cloning is tested on five data sets. The Iris data problem is used as a bench-mark to compare the selective cloning technique with the conventional GA and the back-propagation algorithm. For comparative analysis, same neural network architecture is used for both the back propagation and the genetic algorithms. The selective cloning approach is based on the schema theorem. By using selective cloning, it has been shown that GA is 27.78% more efficient than the conventional GA and 83.33% more efficient than the back propagation approach. The results of selective cloning on other data sets are also discussed.

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A Novel Learning Algorithm of Back-Propagation Neural Network

Cited by 7 publications

References 5 publications

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

Dynamic training rate for backpropagation learning algorithm

Performance Enhancement of Data Classification using Selectively Cloned Genetic Algorithm for Neural Network

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