School of Electrical and Electronic Engineering, Shanghai Institute of Technology, 100 Haiquan Road, Shanghai, China.
School of Electrical and Electronic Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China.
Comput Intell Neurosci. 2021 Aug 21;2021:7529893. doi: 10.1155/2021/7529893. eCollection 2021.
Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT-CNN ensemble model to classify cancer cells images and normal cells images to assist in the diagnosis of acute lymphoblastic leukemia. The ViT-CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model. The vision transformer model is an image classification model based entirely on the transformer structure, which has completely different feature extraction method from the CNN model. The ViT-CNN ensemble model can extract the features of cells images in two completely different ways to achieve better classification results. In addition, the data set used in this article is an unbalanced data set and has a certain amount of noise, and we propose a difference enhancement-random sampling (DERS) data enhancement method, create a new balanced data set, and use the symmetric cross-entropy loss function to reduce the impact of noise in the data set. The classification accuracy of the ViT-CNN ensemble model on the test set has reached 99.03%, and it is proved through experimental comparison that the effect is better than other models. The proposed method can accurately distinguish between cancer cells and normal cells and can be used as an effective method for computer-aided diagnosis of acute lymphoblastic leukemia.
急性淋巴细胞白血病（ALL）是一种致命的癌症，不仅影响成年人，而且约占儿童癌症的 25%。癌症的及时和准确诊断是有效治疗以提高生存率的重要前提。由于白血病 B 淋巴细胞母细胞（癌细胞）在显微镜下的形态与正常 B 淋巴细胞前体（正常细胞）非常相似，因此很难区分癌细胞和正常细胞。因此，我们提出了 ViT-CNN 集成模型来对癌细胞图像和正常细胞图像进行分类，以协助诊断急性淋巴细胞白血病。ViT-CNN 集成模型是一种结合了视觉转换器模型和卷积神经网络（CNN）模型的集成模型。视觉转换器模型是一种完全基于转换器结构的图像分类模型，与 CNN 模型具有完全不同的特征提取方法。ViT-CNN 集成模型可以通过两种完全不同的方式提取细胞图像的特征，从而实现更好的分类效果。此外，本文所用的数据集是一个不平衡数据集，并且存在一定数量的噪声，我们提出了一种差异增强随机采样（DERS）数据增强方法，创建了一个新的平衡数据集，并使用对称交叉熵损失函数来减少数据集噪声的影响。ViT-CNN 集成模型在测试集上的分类准确率达到了 99.03%，通过实验比较证明效果优于其他模型。所提出的方法可以准确地区分癌细胞和正常细胞，可以作为急性淋巴细胞白血病计算机辅助诊断的有效方法。
