使用基于经验里埃分解的分数离散变换检测乳腺癌
1Biomedical Engineering Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Bio-medical materials and engineering
|June 21, 2025
概括
新的分数转换与反复的神经网络相结合,显著提高了乳腺癌检测准确度. 这种先进的方法实现了100%的准确性,帮助医生在早期诊断乳腺癌.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 乳腺癌仍然是全球女性死亡的主要原因.
- 早期检测对于改善患者生存率至关重要.
- 当前的诊断方法和传统的机器学习方法在分类准确性方面存在局限性.
研究的目的:
- 为增强乳腺癌图像分析开发新的分数转换.
- 为了提高计算机辅助乳腺癌诊断的准确性.
- 将新方法与传统方法的性能进行比较.
主要方法:
- 新型分数变换的应用 (从分数里叶变换和离散正弦/正弦转换衍生) 来用于乳腺癌图像.
- 使用实证里埃分解 (EFD) 和统计测量 (平均值,方差,曲率,斜率) 的特征提取.
- 使用循环神经网络双向长期短期记忆 (RNN-BILSTM) 模型进行分类.
主要成果:
- 提出的方法,特别是使用分数离散变换 (方法 4),实现了完美的分类.
- 实现了100%的准确性,灵敏度,特异性,精度,G-平均值和F-测量.
- 在接收器运行特征曲线 (AUC) 下显示出1.0的优越区域.
结论:
- 新型分数变换与RNN-BILSTM相结合,为乳腺癌图像分类提供了一种高度有效的方法.
- 这种先进的技术在诊断准确性方面超过了传统的机器学习方法.
- 开发的方法有望用于临床实施,以帮助医生准确诊断乳腺癌.
相关概念视频
Discrete Fourier Transform
419
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
419
Fast Fourier Transform
482
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
The computational efficiency of the FFT becomes...
482
Discrete-Time Fourier Series
374
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
374
Basic signals of Fourier Transform
594
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
594
Discrete-time Fourier transform
493
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
One of the notable...
493
Continuous -time Fourier Transform
419
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
419


