| _version_ | 1866902077656006656 |
|---|---|
| author | Alawnah, Al-mo'men bellah |
| author_facet | Alawnah, Al-mo'men bellah |
| contents | <p>Abstract: Colorectal polyps are precursors to colorectal cancer, making their early detection<br>and classification crucial for effective treatment. This study focuses on developing<br>Convolutional Neural Networks (CNNs) to classify the shapes of colorectal polyps in<br>endoscopic images. Various CNN architectures, including ResNet101, VGG16, and a custom<br>CNN, were trained and evaluated on the Kvasir dataset, which consists of annotated endoscopic<br>images classified into flat, sessile, and pedunculated polyps.<br>The dataset was divided into 75% for training and 25% for testing, with preprocessing<br>techniques such as resizing, normalization, and augmentation applied to enhance model<br>performance. Transfer learning was leveraged to enhance feature extraction, and the models<br>were evaluated using metrics such as accuracy, recall, precision, and F1-score.<br>Experimental results showed that ResNet101 achieved the highest accuracy of 95.15%,<br>followed by VGG16 (94.85%) and the custom CNN (94.24%). Models such as InceptionV3 and<br>Xception exhibited comparatively lower performance. These findings suggest that CNNs,<br>particularly ResNet101, can effectively classify colorectal polyp shapes, aiding in early<br>diagnosis and treatment. Future work will focus on hyperparameter optimization, ensemble<br>learning, and advanced deep learning techniques to further enhance classification accuracy</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15128796 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Colorectal Polyps Shape Classification Based On CNN Models Alawnah, Al-mo'men bellah Colorectal polyps shape, Deep learning, CNN models, PARIS classification. <p>Abstract: Colorectal polyps are precursors to colorectal cancer, making their early detection<br>and classification crucial for effective treatment. This study focuses on developing<br>Convolutional Neural Networks (CNNs) to classify the shapes of colorectal polyps in<br>endoscopic images. Various CNN architectures, including ResNet101, VGG16, and a custom<br>CNN, were trained and evaluated on the Kvasir dataset, which consists of annotated endoscopic<br>images classified into flat, sessile, and pedunculated polyps.<br>The dataset was divided into 75% for training and 25% for testing, with preprocessing<br>techniques such as resizing, normalization, and augmentation applied to enhance model<br>performance. Transfer learning was leveraged to enhance feature extraction, and the models<br>were evaluated using metrics such as accuracy, recall, precision, and F1-score.<br>Experimental results showed that ResNet101 achieved the highest accuracy of 95.15%,<br>followed by VGG16 (94.85%) and the custom CNN (94.24%). Models such as InceptionV3 and<br>Xception exhibited comparatively lower performance. These findings suggest that CNNs,<br>particularly ResNet101, can effectively classify colorectal polyp shapes, aiding in early<br>diagnosis and treatment. Future work will focus on hyperparameter optimization, ensemble<br>learning, and advanced deep learning techniques to further enhance classification accuracy</p> |
| title | Colorectal Polyps Shape Classification Based On CNN Models |
| topic | Colorectal polyps shape, Deep learning, CNN models, PARIS classification. |
| url | https://doi.org/10.5281/zenodo.15128796 |