  Early diagnosis of the cancer cells is necessary for making an effective
treatment plan and for the health and safety of a patient. Nowadays, doctors
usually use a histological grade that pathologists determine by performing a
semi-quantitative analysis of the histopathological and cytological features of
hematoxylin-eosin (HE) stained histopathological images. This research
contributes a potential classification model for cancer prognosis to
efficiently utilize the valuable information underlying the HE-stained
histopathological images. This work uses the PatchCamelyon benchmark datasets
and trains them in a multi-layer perceptron and convolution model to observe
the model's performance in terms of precision, Recall, F1 Score, Accuracy, and
AUC Score. The evaluation result shows that the baseline convolution model
outperforms the baseline MLP model. Also, this paper introduced ResNet50 and
InceptionNet models with data augmentation, where ResNet50 is able to beat the
state-of-the-art model. Furthermore, the majority vote and concatenation
ensemble were evaluated and provided the future direction of using transfer
learning and segmentation to understand the specific features.
