A Comparative Study on Polyp Classification using Convolutional Neural Networks
Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called 'polyp'. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationObjectSimilar Papers 제목 키워드 기반
Vision Language Models versus Machine Learning Models Performance on Polyp Detection and Classification in Colonoscopy Images
Introduction: This study provides a comprehensive performance assessment of vision-language models (VLMs) against established convolutional neural networks (CNNs) and classic machine learning models (CMLs) for computer-a…
Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations
Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a rel…
ClassificationGeneral Classificationobject-detectionObject DetectionDiagnosing Colorectal Polyps in the Wild with Capsule Networks
Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the resection and histopathological analysi…
image-classificationImage ClassificationDeep domain adaptation for polyphonic melody extraction
Extraction of the predominant pitch from polyphonic audio is one of the fundamental tasks in the field of music information retrieval and computational musicology. To accomplish this task using machine learning, a large …
Domain AdaptationInformation RetrievalMelody ExtractionMeta-Learning+2Feature Selection Gates with Gradient Routing for Endoscopic Image Computing
To address overfitting and enhance model generalization in gastroenterological polyp size assessment, our study introduces Feature Selection Gates (FSG) alongside Gradient Routing (GR) for dynamic feature selection. This…
Binary ClassificationColorectal Polyps Characterizationfeature selection