… In this study, a large dataset of lung and colon histopathology images was … detect lung
and colon cancer accurately and efficiently. Transfer learning is employed for this detection …

… [27] through training of a CNN via transfer learning. The transfer learning step has a training
dataset of 100 thousand histopathological images, divided into nine types of tissue. Another …

… [12] proposed a CNN-based model for gland segmentation in H&E stained histopathological
images of colorectal cancer. In the proposed architecture, the learned gland-separating …

… The softmax-based model, the SVM-based model, and the proposed synthetic model are
tested and compared on a binary breast cancer dataset and a multiclass lung and colon cancer …

… , we have resorted to the use of transfer learning techniques. This strategy comes with the
… The testing and evaluation of such models on our AiCOLO colon cancer dataset ensure …

… automated system capable of accurately identifying subtypes of colon and lung cancer from
histopathological images utilizing transfer learning models. Additionally, the study seeks to …

… in the classification of histopathological images, we applied transfer learning techniques to
four … Transfer Learning In deep learning, a high-performance model needs to be trained, then …

… By finetuning the pre-trained model on our dataset of lung and colon histopathology images,
we can train a highly accurate classifier capable of distinguishing between cancerous and …

… neural networN model, leveraging transfer learning for histopathological image analysis in
lung and colon cancer diagnosis. Our dataset, comprising 25,000 images across five classes, …

… ii) Transfer learning approach using pre-trained Deep CNN networks with … for lung and
colon cancer histopathological images classification. In the proposed study, the transfer learning …