# R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration

arXiv: https://arxiv.org/abs/2509.16251 · HTML: https://mlsanity.com/paper/2509.16251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [GANs for Medical Image Analysis](https://mlsanity.com/paper/1809.06222/) (2018)
- [EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks](https://mlsanity.com/paper/2212.00532/) (2022)
- [Comparison of fine-tuning strategies for transfer learning in medical image classification](https://mlsanity.com/paper/2406.10050/) (2024)
- [Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning](https://mlsanity.com/paper/2310.16954/) (2023)
- [A Comprehensive Literature Review on Sweet Orange Leaf Diseases](https://mlsanity.com/paper/2312.01756/) (2023)
- [A Semantic Segmentation Approach on Sweet Orange Leaf Diseases Detection Utilizing YOLO](https://mlsanity.com/paper/2409.06671/) (2024)
- [A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)](https://mlsanity.com/paper/2409.06687/) (2024)
- [A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection](https://mlsanity.com/paper/2409.06699/) (2024)

## Abstract (arXiv)

State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) are criticized for their extensive computational power, long training times, and large datasets. To overcome this limitation, we propose a reasonable network (R-Net), a lightweight CNN only to detect and classify colorectal cancer (CRC) using the Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset (EBHI). Furthermore, six SOTA CNNs, including Multipath-based CNNs (DenseNet121, ResNet50), Depth-based CNNs (InceptionV3), width-based multi-connection CNNs (Xception), depth-wise separable convolutions (MobileNetV2), spatial exploitation-based CNNs (VGG16), Transfer learning, and two ensemble models are also tested on the same dataset. The ensemble models are a multipath-depth-width combination (DenseNet121-InceptionV3-Xception) and a multipath-depth-spatial combination (ResNet18-InceptionV3-VGG16). However, the proposed R-Net lightweight achieved 99.37% accuracy, outperforming MobileNet (95.83%) and ResNet50 (96.94%). Most importantly, to understand the decision-making of R-Net, Explainable AI such as SHAP, LIME, and Grad-CAM are integrated to visualize which parts of the EBHI image contribute to the detection and classification process of R-Net. The main novelty of this research lies in building a reliable, lightweight CNN R-Net that requires fewer computing resources yet maintains strong prediction results. SOTA CNNs, transfer learning, and ensemble models also extend our knowledge on CRC classification and detection. XAI functionality and the impact of pixel intensity on correct and incorrect classification images are also some novelties in CRC detection and classification.