Artificial intelligence is constantly evolving and can provide effective help
in all aspects of people's lives. The experiment is mainly to study the use of
artificial intelligence in the field of medicine. The purpose of this
experiment was to compare which of MobileNetV1 and MobileNetV2 models was
better at detecting histopathological images of the breast downloaded at
Kaggle. When the doctor looks at the pathological image, there may be errors
that lead to errors in judgment, and the observation speed is slow. Rational
use of artificial intelligence can effectively reduce the error of doctor
diagnosis in breast cancer judgment and speed up doctor diagnosis. The dataset
was downloaded from Kaggle and then normalized. The basic principle of the
experiment is to let the neural network model learn the downloaded data set.
Then find the pattern and be able to judge on your own whether breast tissue is
cancer. In the dataset, benign tumor pictures and malignant tumor pictures have
been classified, of which 198738 are benign tumor pictures and 78, 786 are
malignant tumor pictures. After calling MobileNetV1 and MobileNetV2, the
dataset is trained separately, the training accuracy and validation accuracy
rate are obtained, and the image is drawn. It can be observed that MobileNetV1
has better validation accuracy and overfit during MobileNetV2 training. From
the experimental results, it can be seen that in the case of processing this
dataset, MobileNetV1 is much better than MobileNetV2.

研究人工智能在医学领域的应用，主要比较 MobileNetV1 和 MobileNetV2 模型在检测 Kaggle 下载的乳腺组织病理图像方面的表现，结果显示在处理该数据集的情况下，MobileNetV1 优于 MobileNetV2。