# C-Net: A Reliable Convolutional Neural Network for Biomedical Image Classification

arXiv: https://arxiv.org/abs/2011.00081 · HTML: https://mlsanity.com/paper/2011.00081/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach](https://mlsanity.com/paper/2308.11877/) (2023)
- [TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction](https://mlsanity.com/paper/2307.10003/) (2023)
- [MultiNet with Transformers: A Model for Cancer Diagnosis Using Images](https://mlsanity.com/paper/2301.09007/) (2023)
- [Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification](https://mlsanity.com/paper/2310.09981/) (2023)
- [Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images](https://mlsanity.com/paper/2411.00832/) (2024)

## Abstract (arXiv)

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics.