@Article{oncologie.2020.013870,
AUTHOR = {Pallabi Sharma, Kangkana Bora, Kunio Kasugai, Bunil Kumar Balabantaray},
TITLE = {Two Stage Classification with CNN for Colorectal Cancer Detection},
JOURNAL = {Oncologie},
VOLUME = {22},
YEAR = {2020},
NUMBER = {3},
PAGES = {129--145},
URL = {http://www.techscience.com/oncologie/v22n3/40578},
ISSN = {1765-2839},
ABSTRACT = {In this paper, we address a current problem in medical image processing, 
the detection of colorectal cancer from colonoscopy videos. According to 
worldwide cancer statistics, colorectal cancer is one of the most common cancers. 
The process of screening and the removal of pre-cancerous cells from the large 
intestine is a crucial task to date. The traditional manual process is dependent on 
the expertise of the medical practitioner. In this paper, a two-stage classification is 
proposed to detect colorectal cancer. In the first stage, frames of colonoscopy video 
are extracted and are rated as significant if it contains a polyp, and these results are 
then aggregated in a second stage to come to an overall decision concerning the 
final classification of that frame to be neoplastic and non-neoplastic. In doing so, 
a comparative study is being made by considering the applicability of deep 
learning to perform this two-stage classification. The CNN models namely VGG16, 
VGG19, Inception V3, Xception, GoogLeNet, ResNet50, ResNet100, DenseNet, 
NASNetMobile, MobilenetV2, InceptionResNetV2 and fine-tuned version of each 
model is evaluated. It is observed that the VGG19 model is the best deep learning 
method for colonoscopy image diagnosis.},
DOI = {10.32604/oncologie.2020.013870}
}