The advancement of computer-aided detection systems had a significant impact
on clinical analysis and decision-making on human disease. Lung cancer requires
more attention among the numerous diseases being examined because it affects
both men and women, increasing the mortality rate. LeNet, a deep learning
model, is used in this study to detect lung tumors. The studies were run on a
publicly available dataset made up of CT image data (IQ-OTH/NCCD).
Convolutional neural networks (CNNs) were employed in the experiment for
feature extraction and classification. The proposed system was evaluated on
Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases datasets
the success percentage was calculated as 99.51%, sensitivity (93%) and
specificity (95%), and better results were obtained compared to the existing
methods. Development and validation of algorithms such as ours are important
initial steps in the development of software suites that could be adopted in
routine pathological practices and potentially help reduce the burden on
pathologists.

使用 LeNet 和卷积神经网络在公开数据集上进行的肺肿瘤检测实验中，成功率为 99.51％，敏感性为 93％，特异性为 95％，并且与现有方法相比得到了更好的结果。

评估 LeNet 算法在伊拉克肿瘤教学医院 / 国家癌症疾病中心的肺癌分类中的应用

Evaluating LeNet Algorithms in Classification Lung Cancer from  Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases

Early detection of breast cancer in mammography screening via deep-learning
based computer-aided detection systems shows promising potential in improving
the curability and mortality rates of breast cancer. However, many clinical
centres are restricted in the amount and heterogeneity of available data to
train such models to (i) achieve promising performance and to (ii) generalise
well across acquisition protocols and domains. As sharing data between centres
is restricted due to patient privacy concerns, we propose a potential solution:
sharing trained generative models between centres as substitute for real
patient data. In this work, we use three well known mammography datasets to
simulate three different centres, where one centre receives the trained
generator of Generative Adversarial Networks (GANs) from the two remaining
centres in order to augment the size and heterogeneity of its training dataset.
We evaluate the utility of this approach on mammography patch classification on
the test set of the GAN-receiving centre using two different classification
models, (a) a convolutional neural network and (b) a transformer neural
network. Our experiments demonstrate that shared GANs notably increase the
performance of both transformer and convolutional classification models and
highlight this approach as a viable alternative to inter-centre data sharing.

通过研究利用深度学习的计算机辅助检测系统尽早检测乳腺癌在改善疗效和降低死亡率方面具有潜在优势，但限于数据的数量和异质性，我们提出了使用生成敌对网络的训练生成器来从多个中心共享数据，以此作为代替真实患者数据的潜在解决方案，在两种分类模型下，我们的实验表明，共享的生成敌对网络显著提高了变压器和卷积分类模型的性能，突出了这种方法作为中心之间数据共享的可行替代方法。