  Cancer is the uncontrollable cell division of abnormal cells inside the human
body, which can spread to other body organs. It is one of the non-communicable
diseases (NCDs) and NCDs accounts for 71% of total deaths worldwide whereas
lung cancer is the second most diagnosed cancer after female breast cancer.
Cancer survival rate of lung cancer is only 19%. There are various methods for
the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy
and biopsy. However, to know the subtype of lung cancer based on the tissue
type H and E staining is widely used, where the staining is done on the tissue
aspirated from a biopsy. Studies have reported that the type of histology is
associated with prognosis and treatment in lung cancer. Therefore, early and
accurate detection of lung cancer histology is an urgent need and as its
treatment is dependent on the type of histology, molecular profile and stage of
the disease, it is most essential to analyse the histopathology images of lung
cancer. Hence, to speed up the vital process of diagnosis of lung cancer and
reduce the burden on pathologists, Deep learning techniques are used. These
techniques have shown improved efficacy in the analysis of histopathology
slides of cancer. Several studies reported the importance of convolution neural
networks (CNN) in the classification of histopathological pictures of various
cancer types such as brain, skin, breast, lung, colorectal cancer. In this
study tri-category classification of lung cancer images (normal, adenocarcinoma
and squamous cell carcinoma) are carried out by using ResNet 50, VGG-19,
Inception_ResNet_V2 and DenseNet for the feature extraction and triplet loss to
guide the CNN such that it increases inter-cluster distance and reduces
intra-cluster distance.
