Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sensors (Basel). 2022 Jun 30;22(13):4938. doi: 10.3390/s22134938.
One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.
医疗行业和科学界最有前途的研究领域之一是专注于基于人工智能的应用，以解决实际的医疗挑战，例如构建用于乳腺癌的计算机辅助诊断 (CAD) 系统。迁移学习是最近出现的基于人工智能的技术之一，它可以实现快速的学习进展并提高医学成像诊断性能。尽管基于深度学习的乳腺癌分类已经得到了广泛的研究，但仍存在一些障碍需要研究从乳腺 X 光片中提取的高级深度特征之间的独立性。本研究解决了在设计用于乳腺病变分类的有效 CAD 系统时仍然存在的两个挑战。第一个挑战是通过生成伪彩色图像而不是仅使用输入原始灰度图像来丰富深度学习模型的输入信息。为了实现这一目标，并行使用了两种不同的图像预处理技术：限制对比度自适应直方图均衡化 (CLAHE) 和像素级强度调整。原始图像保留在第一个通道中，而另外两个通道分别接收处理后的图像。生成的三通道伪彩色图像直接输入到骨干 CNN 的输入层，以生成更强大的高级深度特征。第二个挑战是克服深度学习模型生成的高度相关深度特征之间出现的多重共线性问题。提出了一种新的基于逻辑回归 (LR) 和主成分分析 (PCA) 的混合处理技术，并称为 LR-PCA。这样的过程有助于选择显著的主成分 (PCs) 以进一步用于分类目的。该 CAD 系统已使用 INbreast 和 mini-MAIS 两个不同的公共基准数据集进行了检查。该 CAD 系统在使用 INbreast 和 mini-MAIS 数据集时分别能够达到 98.60%和 98.80%的最高性能精度。这样的 CAD 系统似乎对乳腺癌诊断有用且可靠。
