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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Colon and Lung cancer is one of the most perilous and dangerous ailments thatindividuals are enduring worldwide and has become a general medical problem. Tolessen the risk of death, a legitimate and early finding is particularlyrequired. In any case, it is a truly troublesome task that depends on theexperience of histopathologists. If a histologist is under-prepared it may evenhazard the life of a patient. As of late, deep learning has picked up energy,and it is being valued in the analysis of Medical Imaging. This paper intendsto utilize and alter the current pre-trained CNN-based model to identify lungand colon cancer utilizing histopathological images with better augmentationtechniques. In this paper, eight distinctive Pre-trained CNN models, VGG16,NASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet,and DenseNet169 are trained on LC25000 dataset. The model performances areassessed on precision, recall, f1score, accuracy, and auroc score. The resultsexhibit that all eight models accomplished noteworthy results ranging from 96%to 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picturethe attention images of Pre-trained CNN models classifying malignant and benignimages.

cornell university

Prediction of lung and colon cancer through analysis of  histopathological images by utilizing Pre-trained CNN models with  visualization of class activation and saliency maps