Multiclass Anomaly Detection in GI Endoscopic Images using Optimized Deep One-class Classification in an Imbalanced Dataset

• URL: http://arxiv.org/abs/2103.08508v1
• Date: Mon, 15 Mar 2021 16:28:42 GMT
• Title: Multiclass Anomaly Detection in GI Endoscopic Images using Optimized Deep One-class Classification in an Imbalanced Dataset
• Authors: Mohammad Reza Mohebbian, Seyed Shahim Vedaei, Khan A. Wahid and Paul Babyn
• Abstract summary: Wireless Capsule Endoscopy helps physicians examine the gastrointestinal (GI) tract noninvasively. Many available datasets, such as KID2 and Kvasir, suffer from imbalance issue which make it difficult to train an effective artificial intelligence (AI) system. In this study, an ensemble of one-class classifiers is used for detecting anomaly.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wireless Capsule Endoscopy helps physicians examine the gastrointestinal (GI) tract noninvasively, with the cost of generating many images. Many available datasets, such as KID2 and Kvasir, suffer from imbalance issue which make it difficult to train an effective artificial intelligence (AI) system. Moreover, increasing number of classes makes the problem worse. In this study, an ensemble of one-class classifiers is used for detecting anomaly. This method focuses on learning single models using samples from only one class, and ensemble all models for multiclass classification. A total of 1,778 normal, 227 inflammation, 303 vascular diseases, and 44 polyp images have been used from the KID2 dataset. In the first step, deep features are extracted based on an autoencoder architecture from the preprocessed images. Then, these features are oversampled using Synthetic Minority Over-sampling Technique and clustered using Ordering Points to Identify the Clustering Structure. To create one-class classification model, the Support Vector Data Descriptions are trained on each cluster with the help of Ant Colony Optimization, which is also used for tuning clustering parameters for improving F1-score. This process is applied on each classes and ensemble of final models used for multiclass classification. The entire algorithm ran 5 times and obtained F1-score 96.3 +- 0.2% and macro-average F1-score 85.0 +- 0.4%, for anomaly detection and multiclass classification, respectively. The results are compared with GoogleNet, AlexNet, Resnet50, VGG16 and other published algorithms, and demonstrate that the proposed method is a competitive choice for multiclass class anomaly detection in GI images.

Related papers

• FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification [0.0]
  This work offers a strong methodology for classifying endoscopic images. We suggest a hybrid feature extraction method that combines convolutional neural networks (CNNs), multi-layer perceptrons (MLPs), and radiomics. We have achieved a validation accuracy of 76.2% in the capsule endoscopy video frame classification task.
  arXiv  Detail & Related papers  (2024-11-04T21:55:52Z)
• Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification [57.1795052451257]
  We study the dependence of the GAN-based augmentation performance on dataset size with a focus on small samples. We train StyleGAN2-ADA with both sets and then, after validating the quality of generated images, we use trained GANs as one of the augmentations approaches in multi-class classification problems. The GAN-based augmentation approach is found to be comparable with classical augmentation in the case of medium and large datasets but underperforms in the case of smaller datasets.
  arXiv  Detail & Related papers  (2024-01-26T08:28:13Z)
• Convolutional autoencoder-based multimodal one-class classification [80.52334952912808]
  One-class classification refers to approaches of learning using data from a single class only. We propose a deep learning one-class classification method suitable for multimodal data.
  arXiv  Detail & Related papers  (2023-09-25T12:31:18Z)
• Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification [31.539129126161978]
  This paper introduces a novel approach called the Sub-Cluster-Aware Network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SCAN is the observation that skin disease images within a class often exhibit multiple sub-clusters. We evaluate the proposed approach on two public datasets for few-shot skin disease classification.
  arXiv  Detail & Related papers  (2022-07-03T16:06:04Z)
• Deep embedded clustering algorithm for clustering PACS repositories [0.0]
  We tackle the problem of fully-unsupervised clustering of medical images using pixel data only. We train the models on 30,000 images, and test them using a separate test set consisting of 8,000 images.
  arXiv  Detail & Related papers  (2022-06-24T05:57:49Z)
• Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology [9.037868656840736]
  In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features. This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions. Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
  arXiv  Detail & Related papers  (2022-04-18T13:31:53Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification [1.6986898305640261]
  We propose a fully automated computer-aided diagnosis tool for classifying single-cell and slide images of cervical cancer. We use the Sugeno Fuzzy Integral to ensemble the decision scores from three popular deep learning models, namely, Inception v3, DenseNet-161 and ResNet-34.
  arXiv  Detail & Related papers  (2021-08-21T08:41:41Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• SCAN: Learning to Classify Images without Labels [73.69513783788622]
  We advocate a two-step approach where feature learning and clustering are decoupled. A self-supervised task from representation learning is employed to obtain semantically meaningful features. We obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime.
  arXiv  Detail & Related papers  (2020-05-25T18:12:33Z)
• Unsupervised Person Re-identification via Softened Similarity Learning [122.70472387837542]
  Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information. Experiments on two image-based and video-based datasets demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-04-07T17:16:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.