… This research work presents lung cancer detection using histopathological images. A
convolutional neural network (CNN) was implemented to classify an image of three different …

… the applications of CNNs to histopathological images of lung cancer. Furthermore, no public
… on lung cancer detection was only on cytological image [48]. The size of each image was …

… based method for detection of lung cancer in whole slide histopathology images. VGG16 and
… have potential to perform lung cancer diagnose from whole slide images, but more effort is …

… Image classification is a challenging task for the visual content, particularly microscopic images
for example histopathological images … textures present in histopathology of images. Deep …

… image feature maps with histologic phenotype in this cohort. The goal of this work was to
non-invasively predict lung cancer histology … clinically important histologic subtypes in NSCLC. …

… Classification and mutation prediction from non–small cell lung cancer histopathology images
using deep learning. Nature medicine, 24(10):1559–1567, 2018. 6 [5] Michaël Defferrard, …

… cancer … cancer WSIs through the use of both UNet and SegNet models. We introduce a
multi-step training strategy as a remedy for the sparse annotation of histopathological images. …

… image dataset (LC25000) with 25,000 color images in 5 classes. Each class contains 5,000
images of the following histologic … colonic tissue, lung adenocarcinoma, lung squamous cell …

… BK Hatuwal and HC Thapa employed a convolutional neural network to classify Lung
cancer histopathology images and achieved an accuracy of 96.11% and 97.2% for training and …

… binary classification tasks, ie, the classification between lung cancer subtypes, identifying
malignant and benign colon as well as lung histological images. We utilized test data for test …