Outlier Detection in Gastrointestinal Tract Images using Machine Learning Algorithms

Abstract

This study explores the effectiveness of Convolutional Neural Networks (CNNs) and DenseNet models for binary and multi-class classification tasks using the Kvasir dataset, which consists of gastrointestinal tract images. The research aims to perform a com parative analysis of CNN and DenseNet models on both raw and refined datasets. Initially, binary and multi-class classifications were conducted on the original dataset to establish baseline performances for both models. Following this, K-Means Cluster ing was applied for outlier detection to refine the dataset by removing anomalies. The refined dataset was then used to re-evaluate the models’ performances on the same classification tasks. The results demonstrated that CNN outperformed DenseNet in binary classifica tion tasks, achieving an accuracy of 91.1% on raw data and 91.0% on refined data, while DenseNet’s accuracy dropped from 83.5% to 73.1% after refinement. For multi class classification, DenseNet performed better on the raw dataset with an accuracy of 84%, compared to CNN’s 74%. However, after data refinement, DenseNet’s perfor mance decreased slightly to 79%, whereas CNN showed a minor improvement, achieving 78%. The data refinement process, involving outlier removal, did not significantly af fect the overall performance of the CNN model but had a notable negative impact on DenseNet, especially in binary classification tasks. These findings suggest that while CNN is robust across both binary and multi-class tasks with or without data refine ment, DenseNet is more sensitive to changes in the dataset. This study highlights the importance of dataset refinement and its varying impact on different neural network architectures in medical image classification.