English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Artificial intelligence is constantly evolving and can provide effective helpin all aspects of people's lives. The experiment is mainly to study the use ofartificial intelligence in the field of medicine. The purpose of thisexperiment was to compare which of MobileNetV1 and MobileNetV2 models wasbetter at detecting histopathological images of the breast downloaded atKaggle. When the doctor looks at the pathological image, there may be errorsthat lead to errors in judgment, and the observation speed is slow. Rationaluse of artificial intelligence can effectively reduce the error of doctordiagnosis in breast cancer judgment and speed up doctor diagnosis. The datasetwas downloaded from Kaggle and then normalized. The basic principle of theexperiment 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 iscancer. In the dataset, benign tumor pictures and malignant tumor pictures havebeen classified, of which 198738 are benign tumor pictures and 78, 786 aremalignant tumor pictures. After calling MobileNetV1 and MobileNetV2, thedataset is trained separately, the training accuracy and validation accuracyrate are obtained, and the image is drawn. It can be observed that MobileNetV1has better validation accuracy and overfit during MobileNetV2 training. Fromthe experimental results, it can be seen that in the case of processing thisdataset, MobileNetV1 is much better than MobileNetV2.

Study for Performance of MobileNetV1 and MobileNetV2 Based on Breast Cancer