RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification

TL;DR

RCCNet is a simple, efficient CNN architecture designed for classifying colon cancer nuclei, achieving high accuracy with fewer parameters and faster training compared to complex models.

Contribution

This paper introduces RCCNet, a lightweight CNN model for histological nuclei classification that outperforms or matches state-of-the-art models in efficiency and accuracy.

Findings

Achieved 80.61% classification accuracy.

Reduced model complexity with only 1.5 million parameters.

Faster training time and better generalization.

Abstract

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.