ArticleJournal of medical systems2019
An Enhancement of Computer Aided Approach for Colon Cancer Detection in WCE Images Using ROI Based Color Histogram and SVM2.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 36 citations in OpenAlex.
- Colonoscopy polyp classification via enhanced scattering wavelet Convolutional Neural Network.PloS one · 2024Article
- Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.Sensors (Basel, Switzerland) · 2022Review
- Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care.Contrast media & molecular imaging · 2022Article
- An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy.Frontiers in genetics · 2022Article
- WCE polyp detection based on novel feature descriptor with normalized variance locality-constrained linear coding.International journal of computer assisted radiology and surgery · 2020Article
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The colon cancer is formed by uncontrollable growth of abnormal cells in large intestine or colon that can affect both men and women and it is third cancer disease in the world. At present, Wireless Capsule Endoscopy (WCE) screening method is utilized to identify colon cancer tumor at early stage to save the patient life who affected by the colon cancer. In this CTC method, the radiologist needs to analyze the colon polyps in digital image using computer aided approach with accurate automatic tumor classification to detect the cancer tumor at early stage. This kind of computer aided approach can operate as an intermediate between input digital image and radiologist. Therefore, in this paper, a novel computer aided approach is presented with ROI based color histogram and SVM2 to find the cancer tumor in WCE image. In this method, the digital WCE image can be preprocessed using filtering and ROI based color histogram depending on the salient region in colon. In common, the salient region can be distinctive because of low redundancy. Hence, the saliency is estimated by ROI based color histogram on the basis of color and structure contrast in given colon image for the further process of clustering and tumor classification in WCE image. The K-means clustering can be employed to cluster the preprocessed digital image to discover the tumor of colon. Subsequently, the features are extracted from the image in terms of contrast, correlation, energy and homogeneity by applying SGLDM method. The SVM2 classifier as input to classify the tumor is normal or malignancy using selected feature vectors. Here, the extracted features can also being combined to enhance the hybrid feature vector for the accurate tumor classification. Experimental results of proposed method can show that this presented technique can executes can tumor detection in colon image accurately reaching almost 95% in evaluation with existing algorithms.
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