Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

Wang, Shuihua; Muhammad, Khan; Phillips, P. Jonathon; Dong, Zhengchao; Zhang, Yudong

doi:10.1007/s12652-017-0639-5

Cited by 24 publications

(13 citation statements)

References 45 publications

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“…Medical image analysis is a vast area of research, and many researchers have added to the vast variety of subfields of it [12]. We have taken a look at past work on brain tumor classification.…”

Section: Related Workmentioning

confidence: 99%

Incremental Dilations Using CNN for Brain Tumor Classification

2020

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Brain tumor classification is a challenging task in the field of medical image processing.Technology has now enabled medical doctors to have additional aid for diagnosis. We aim toclassify brain tumors using MRI images, which were collected from anonymous patients andartificial brain simulators . In this article, we carry out a comparative study between SimpleArtificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and DilatedConvolutional Neural Networks. The experimental results shed light on the high classificationperformance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from thegridding phenomenon. An incremental, even number dilation rate takes advantage of the reducedcomputational overhead and also overcomes the adverse effects of gridding. Comparative analysisbetween different combinations of dilation rates for the different convolution layers, help validatethe results. The computational overhead in terms of efficiency for training the model to reach anacceptable threshold accuracy of 90% is another parameter to compare the model performance.

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Section: Related Workmentioning

confidence: 99%

Incremental Dilations Using CNN for Brain Tumor Classification

2020

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“…ELM is a single-hidden layer feedforward neural network (SLFNs) proposed by Huang et al The network is constituted of input layer, hidden layer and output layer [20][21][22]. If the activation functions in the hidden layer are infinitely differentiable, the input weights and hidden layer biases can be randomly assigned, ELMs can be simply considered as a linear system.…”

Section: B Extreme Learning Machinementioning

confidence: 99%

Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine

Li¹,

Guo²,

Bao³

et al. 2018

Advances in Intelligent Systems Research

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Abstract-To improve the computer-assisted diagnosis and decision in dentistry, we tested a new method combining Hu moment invariant (HMI) method and extreme learning machine (ELM) to implement the teeth classification in cross-section image of Cone Beam Computed Tomography (CBCT). 160 images were analyzed and 4 categories were recognized. The results showed the sensitivities of incisors, canine, premolar, and molars were 78.25± 6.02%, 78.00± 5.99%, 79.25± 7.91%, and 78.75± 5.17%, better than ANN statistical-significantly.

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“…ELM has better and faster generalisation performance than SVM and backpropagation-based NNs [21,23,24]. Besides, the effectiveness of the ELM has been proven in several medical domains such as ductal carcinoma in situ detection [25] and pathological brain detection [26,27]. In order to further enhance the ELM [28], optimised the input-hidden layer weight and bias using Optimised Genetic Algorithm and named it as Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM).…”

Section: Introductionmentioning

confidence: 99%

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

et al. 2020

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The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension

Cited by 24 publications

References 45 publications

Incremental Dilations Using CNN for Brain Tumor Classification

Incremental Dilations Using CNN for Brain Tumor Classification

Teeth Category Classification via Hu Moment Invariant and Extreme Learning Machine

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

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