  Efficient and precise classification of histological cell nuclei is of utmost
importance due to its potential applications in the field of medical image
analysis. It would facilitate the medical practitioners to better understand
and explore various factors for cancer treatment. The classification of
histological cell nuclei is a challenging task due to the cellular
heterogeneity. This paper proposes an efficient Convolutional Neural Network
(CNN) based architecture for classification of histological routine colon
cancer nuclei named as RCCNet. The main objective of this network is to keep
the CNN model as simple as possible. The proposed RCCNet model consists of only
1,512,868 learnable parameters which are significantly less compared to the
popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The
experiments are conducted over publicly available routine colon cancer
histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet
model are compared with five state-of-the-art CNN models in terms of the
accuracy, weighted average F1 score and training time. The proposed method has
achieved a classification accuracy of 80.61% and 0.7887 weighted average F1
score. The proposed RCCNet is more efficient and generalized terms of the
training time and data over-fitting, respectively.
