Choudhury Avishek, Perumalla Sunanda
School of Systems and Entereprises, Stevens Institute of Technology, Hoboken, NJ, USA.
Clinical and Business Intelligence, Integris Health, Oklahoma City, OK, USA.
Technol Health Care. 2021;29(1):33-43. doi: 10.3233/THC-202226.
One of the most broadly founded approaches to envisage cancer treatment relies upon a pathologist's efficiency to visually inspect the appearances of bio-markers on the invasive tumor tissue section. Lately, deep learning techniques have radically enriched the ability of computers to identify objects in images fostering the prospect for fully automated computer-aided diagnosis. Given the noticeable role of nuclear structure in cancer detection, AI's pattern recognizing ability can expedite the diagnostic process.
In this study, we propose and implement an image classification technique to identify breast cancer.
We implement the convolutional neural network (CNN) on breast cancer image data set to identify invasive ductal carcinoma (IDC).
The proposed CNN model after data augmentation yielded 78.4% classification accuracy. 16% of IDC (-) were predicted incorrectly (false negative) whereas 25% of IDC (+) were predicted incorrectly (false positive).
The results achieved by the proposed approach have shown that it is feasible to employ a convolutional neural network particularly for breast cancer classification tasks. However, a common problem in any artificial intelligence algorithm is its dependence on the data set. Therefore, the performance of the proposed model might not be generalized.
设想癌症治疗的最广泛应用的方法之一依赖于病理学家直观检查浸润性肿瘤组织切片上生物标志物外观的能力。最近，深度学习技术极大地丰富了计算机识别图像中物体的能力，为全自动计算机辅助诊断带来了希望。鉴于核结构在癌症检测中的显著作用，人工智能的模式识别能力可以加快诊断过程。
在本研究中，我们提出并实施一种图像分类技术来识别乳腺癌。
我们在乳腺癌图像数据集上实施卷积神经网络（CNN）以识别浸润性导管癌（IDC）。
数据增强后的所提出的CNN模型产生了78.4%的分类准确率。16%的IDC（-）被错误预测（假阴性），而25%的IDC（+）被错误预测（假阳性）。
所提出方法取得的结果表明，采用卷积神经网络特别是用于乳腺癌分类任务是可行的。然而，任何人工智能算法的一个常见问题是其对数据集的依赖性。因此，所提出模型的性能可能无法推广。
