Breast cancer detection via Hu moment invariant and feedforward neural network

Zhang, Xiaowei; Yang, Jiquan; Nguyen, Elijah

doi:10.1063/1.5033394

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…To facilitate a convenient and intuitive comparison of the breast cancer detection methods, we have presented the comparison results graphically in Figure 5. We conducted a comprehensive comparison of our proposed method with three approaches in the field of breast cancer detection: Hu moment invariant(HMI) approach [23], Support Vector Machine(SVM) Combined with Principal Component Analysis(PCA) [55]and wavelet energy and support vector machine(SVM) [56]. The results are shown in Table 6 and Figure 6.…”

Section: Optimal Structure Of Neural Networkmentioning

confidence: 99%

“…The results are shown in Table 6 and Figure 6. From the point of view of accuracy, the values obtained by HMI [23], SVM+PCA [55], and WEN+SVM [56] are 73.50±1.35%, 82.85±2.21%, and 81.80±0.92% respectively. However, the accuracy of our method reaches 83.17±2.08%.…”

Section: Optimal Structure Of Neural Networkmentioning

confidence: 99%

“…This study [22] utilizes computer-aided diagnosis (CAD) systems and a combination of preprocessing algorithms to improve the accuracy of mammography image interpretation and classification. Nguyen [23] employed Hu moment variant (HMI) approach to detect breast cancer. Rao [24] is the first approach to test Jaya algorithm in breast image classification.…”

Section: Introductionmentioning

confidence: 99%

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Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm

Wang

2023

EAI Endorsed Trans AI Robotics

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Enhancing the precision of breast cancer detection is the primary objective of this investigation, given its status as the most prevalent cancer among women worldwide. Timely identification of breast cancer can significantly improve the likelihood of successful diagnosis. To achieve this, we propose a innovative way that combines wavelet energy and a feedforward neural network. Our method employs the genetic algorithm and undergoes 20 iterations of 10-fold cross-validation for robustness. Via utilizing wavelet energy as a feature extractor and a feedforward neural network as the classifier, our method outperforms three alternative algorithms.

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Section: Optimal Structure Of Neural Networkmentioning

confidence: 99%

Section: Optimal Structure Of Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm

Wang

2023

EAI Endorsed Trans AI Robotics

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“…Therefore, the description of the station logo is the first and most critical step in the station logo detection. At present, the existing domestic standard feature analysis algorithms are: based on colour histogram [5], ordinary Hu invariant moment [6], weighted Hu invariant moment [7], spatial distribution histogram [8] and so on. The TV station logo detection based on colour histogram uses different colour tones between different types of station labels to complete the station logo detection.…”

Section: Introductionmentioning

confidence: 99%

Object detection method on station logo with single shot multi‐box detector

Fei

et al. 2020

J. eng.

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“…Therefore, the description of the station logo is the first and most critical step in the station logo detection. At present, the existing domestic standard feature analysis algorithms are: based on color histogram [1], ordinary Hu invariant moment [2], weighted Hu invariant moment [3], spatial distribution histogram [4] and so on. The TV station logo detection based on color histogram uses different color tones between different types of station labels to complete the station caption detection.…”

Section: Introductionmentioning

confidence: 99%

A Detection algorithm based on Convolutional Neural Network

Fei¹,

Li²,

Xu³

et al. 2018

Preprint

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The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

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Breast cancer detection via Hu moment invariant and feedforward neural network

Cited by 10 publications

References 32 publications

Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm

Breast cancer detection via wavelet energy and feed-forward neural network trained by genetic algorithm

Object detection method on station logo with single shot multi‐box detector

A Detection algorithm based on Convolutional Neural Network

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