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。