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Computer-Aided Polyps Classification from Colonoscopy Using Stacking-Based Deep Learning Model
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  • Shweta Gangrade,
  • Prakash Chandra Sharma,
  • Akhilesh Kumar Sharma,
  • Jayesh Gangrade
Shweta Gangrade
Manipal University Jaipur

Corresponding Author:[email protected]

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Prakash Chandra Sharma
Manipal University Jaipur
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Akhilesh Kumar Sharma
Manipal University Jaipur
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Jayesh Gangrade
Manipal University Jaipur

Abstract

Colorectal cancer is responsible for a high proportion of cancer mortality. The most effective way to avoid colorectal cancer is to have a colonoscopy. However, not every polyp in the colon is prone to cancer. As a result, different techniques are employed to classify polyps. Video endoscopy can diagnose stomach ulcers, bleeding, and polyps. Doctors spend a lot of time reviewing medical video endoscopy images. Computer-aided diagnosis to analyze all images quickly and accurately. Diagnosing gastrointestinal problems with the proposed strategy is novel. Following the classification of the gastrointestinal disorder, the further therapy and surgeries will be determined by its classification. The 5000 images in the Kvasir dataset are evenly distributed across five different digestive tract-related categories: ulcerative colitis, dye-lifted polyps, resection margins, normal cecum, polyps, and ulcerative polyps. The images used in the deep learning networks went through enhancements and noise reduction. We suggested a stacking ensemble approach to boost the model’s accuracy for prediction. The ensemble approach included five meticulously tuned deep convolutional neural network architectures, namely Xception, ResNet-101, VGG-19, EfficientNetB2v3, and MobineNetV2. These models were trained using weights obtained from the ImageNet dataset. Accuracy of 96.50% was achieved using meta models based on logistic regression, K-NN, Decision Tree, Support vector machine, and Naive Bayes classifier.