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

arXiv: https://arxiv.org/abs/1810.02797 · HTML: https://mlsanity.com/paper/1810.02797/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [Structure Embedded Nucleus Classification for Histopathology Images](https://mlsanity.com/paper/2302.11416/) (2023)
- [Cell Graph Transformer for Nuclei Classification](https://mlsanity.com/paper/2402.12946/) (2024)
- [GradMix for nuclei segmentation and classification in imbalanced pathology image datasets](https://mlsanity.com/paper/2210.12938/) (2022)
- [Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification](https://mlsanity.com/paper/2203.04771/) (2022)
- [A review of machine learning approaches, challenges and prospects for computational tumor pathology](https://mlsanity.com/paper/2206.01728/) (2022)
- [Joint Triplet Autoencoder for Histopathological Colon Cancer Nuclei Retrieval](https://mlsanity.com/paper/2105.10262/) (2021)
- [Modified ResNet Model for MSI and MSS Classification of Gastrointestinal Cancer](https://mlsanity.com/paper/2202.01905/) (2022)
- [A Multi-Scale Conditional Deep Model for Tumor Cell Ratio Counting](https://mlsanity.com/paper/2101.11731/) (2021)
- [Semantic Map Injected GAN Training for Image-to-Image Translation](https://mlsanity.com/paper/2112.01845/) (2021)
- [Moment Centralization based Gradient Descent Optimizers for Convolutional Neural Networks](https://mlsanity.com/paper/2207.09066/) (2022)
- [Patch-wise Features for Blur Image Classification](https://mlsanity.com/paper/2304.03156/) (2023)

## Abstract (arXiv)

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.