Breast Tumor Computer-aided Diagnosis using Self-Validating Cerebellar Model Neural Networks

Guan, Jian; Lin, Lo Yi; Guo, Jian; Lin, Chih Min; Le, Tien Loc; Rudas, Imre J.

doi:10.12700/aph.13.4.2016.4.3

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Compared to other methods, this work improved classification accuracy by optimizing the ANN parameters and network structure. Guan et al [8] proposed breast tumor classifier. They used Wisconsin Breast Cancer Dataset to evaluate their diagnostic model called self-validation cerebellar model articulation controller (SVCMAC) neural network.…”

Section: A Related Workmentioning

confidence: 99%

Breast cancer classification using machine learning techniques: a comparative study

Houfani

Slatnia

Kazar

et al. 2020

Mèd Technologies J.

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Background: The second leading deadliest disease affecting women worldwide, after lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed. These approaches show their effectiveness in data classification in many fields, especially in healthcare. Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification. The used dataset is Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave the best results with an accuracy of 98% for breast cancer classification. Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are performant ( good accuracy specificity and sensitivity) compared to the other evaluated algorithms.

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Section: A Related Workmentioning

confidence: 99%

Breast cancer classification using machine learning techniques: a comparative study

Houfani

Slatnia

Kazar

et al. 2020

Mèd Technologies J.

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“…In 1975, Albus proposed the concept of the cerebellar model articulation controller (CMAC) for the first time (Albus, 1975), which was an imitation of the cerebellum learning structure, also one of the local approximations in the neural network system. Cerebellar model network system not only has non-linear approximation ability, adaptive generalization ability and associative memory ability, but also is a kind of fast convergence neural network, which has been widely used in non-linear real-time control system (Guan et al, 2016). An efficient controller was proposed for the robot manipulators based on the structure and local learning characteristics of CMAC (Commuri et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO

et al. 2019

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A robust adaptive recurrent cerebellar model articulation controller (RARC) neural network for non-linear systems using the genetic particle swarm optimization (GPSO) algorithm is presented in this study. The RARC is used as the principal tracking controller and the robust compensation controller is designed to recover the residual of the approximation error. In the RARC neural network, the steepest descent gradient method and the Lyapunov function are used for deriving the adaptive law parameter of the system. Besides, the learning rates play an important role in these adaptive laws and they have a great effect on the functions of control systems. In this paper, the combination of the genetic algorithm with the mutation particle swarm optimization algorithm is applied to seek for the optimal learning rates of the RARC adaptation laws. The numerical simulations about the inverted pendulum system as well as the robot manipulator system are given to confirm the effectiveness and practicability of the GPSO-RARC-based control system. Compared with other control schemes, the proposed control scheme is testified to be reliable and can obtain the optimal parameter about the learning rates and the minimum root mean square error for non-linear systems.

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“…Artificial Neural Networks (ANN) were motivated by the already existing biological structures of the brain [11], having powerful capabilities for tasks such as learning, pattern matching and adaptation. As the real biological brain, the basic construction units of ANNs are artificial neurons connected by weighted edges.…”

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confidence: 99%

Database for Research Projects to Solve the Inverse Heat Conduction Problem

Szénási

Felde

2019

Data

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To achieve the optimal performance of an object to be heat treated, it is necessary to know the value of the Heat Transfer Coefficient (HTC) describing the amount of heat exchange between the work piece and the cooling medium. The prediction of the HTC is a typical Inverse Heat Transfer Problem (IHCP), which cannot be solved by direct numerical methods. Numerous techniques are used to solve the IHCP based on heuristic search algorithms having very high computational demand. As another approach, it would be possible to use machine-learning methods for the same purpose, which are capable of giving prompt estimations about the main characteristics of the HTC function. As known, a key requirement for all successful machine-learning projects is the availability of high quality training data. In this case, the amount of real-world measurements is far from satisfactory because of the high cost of these tests. As an alternative, it is possible to generate the necessary databases using simulations. This paper presents a novel model for random HTC function generation based on control points and additional parameters defining the shape of curve segments. As an additional step, a GPU accelerated finite-element method was used to simulate the cooling process resulting in the required temporary data records. These datasets make it possible for researchers to develop and test their IHCP solver algorithms.

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Breast Tumor Computer-aided Diagnosis using Self-Validating Cerebellar Model Neural Networks

Cited by 6 publications

References 14 publications

Breast cancer classification using machine learning techniques: a comparative study

Breast cancer classification using machine learning techniques: a comparative study

Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO

Database for Research Projects to Solve the Inverse Heat Conduction Problem

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