mCRF and mRD: Two Classification Methods Based on a Novel Multiclass Label Noise Filtering Learning Framework

Xia, Shuyin; Chen, Baiyun; Wang, Guoyin; Zheng, Yong; Gao, Xinbo; Giem, Elisabeth

doi:10.1109/tnnls.2020.3047046

Cited by 33 publications

(11 citation statements)

References 43 publications

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“…Future indications for this work are an expansion of our dataset and inclusion of classification images of NPDR, such as mild, moderate, and severe classes, for more complex classification problems. In addition, some robust classifiers can be discussed to improve accuracy in future research, which can refer to [47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Deep Feature Vectors Concatenation for Eye Disease Detection Using Fundus Image

et al. 2021

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Fundus image is an image that captures the back of the eye (retina), which plays an important role in the detection of a disease, including diabetic retinopathy (DR). It is the most common complication in diabetics that remains an important cause of visual impairment, especially in the young and economically active age group. In patients with DR, early diagnosis can effectively help prevent the risk of vision loss. DR screening was performed by an ophthalmologist by analysing the lesions on the fundus image. However, the increasing prevalence of DR is not proportional to the availability of ophthalmologists who can read fundus images. It can lead to delayed prevention and management of DR. Therefore, there is a need for an automated diagnostic system as it can help ophthalmologists increase the efficiency of the diagnostic process. This paper provides a deep learning approach with the concatenate model for fundus image classification with three classes: no DR, non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). The model architecture used is DenseNet121 and Inception-ResNetV2. The feature extraction results from the two models are combined and classified using the multilayer perceptron (MLP) method. The method that we propose gives an improvement compared to a single model with the results of accuracy, and average precision and recall of 91% and 90% for the F1-score, respectively. This experiment demonstrates that our proposed deep-learning approach is effective for the automatic DR classification using fundus photo data.

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Section: Discussionmentioning

confidence: 99%

Deep Feature Vectors Concatenation for Eye Disease Detection Using Fundus Image

et al. 2021

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“…Guan et al [30] proposed a sequential ensemble noise filter that generated a noise score for each feature instance to identify noisy labels. Xia et al [31] defined relative density based on the idea that samples surrounded by heterogeneous points were more likely to be the noise than homogeneous points to identify noise [10] . Samples with a relative density greater than or equal to the preset threshold were considered mislabeled.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 chest X-ray image classification in the presence of noisy labels

2023

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“…In the case of sensor data, artifacts of the sensor device or noise caused by the environment can degrade signal quality [ 10 , 11 ]. Moreover, manual labeling of collected data may be erroneous owing to mistakes or insufficient information [ 12 , 13 ]. Data collected via crawling may be unintentionally collected or incorrectly labeled [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition

Kim

Lee

2022

Sensors

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This paper proposes a data valuation algorithm for inertial measurement unit-based human activity recognition (IMU-based HAR) data based on meta reinforcement learning. Unlike previous studies that received feature-level input, the algorithm in this study added a feature extraction structure to the data valuation algorithm, and it can receive raw-level inputs and achieve excellent performance. As IMU-based HAR data are multivariate time-series data, the proposed algorithm incorporates an architecture capable of extracting both local and global features by inserting a transformer encoder after the one-dimensional convolutional neural network (1D-CNN) backbone in the data value estimator. In addition, the 1D-CNN-based stacking ensemble structure, which exhibits excellent efficiency and performance on IMU-based HAR data, is used as a predictor to supervise model training. The Berg balance scale (BBS) IMU-based HAR dataset and the public datasets, UCI-HAR, WISDM, and PAMAP2, are used for performance evaluation in this study. The valuation performance of the proposed algorithm is observed to be excellent on IMU-based HAR data. The rate of discovering corrupted data is higher than 96% on all datasets. In addition, classification performance is confirmed to be improved by the suppression of discovery of low-value data.

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mCRF and mRD: Two Classification Methods Based on a Novel Multiclass Label Noise Filtering Learning Framework

Cited by 33 publications

References 43 publications

Deep Feature Vectors Concatenation for Eye Disease Detection Using Fundus Image

Deep Feature Vectors Concatenation for Eye Disease Detection Using Fundus Image

COVID-19 chest X-ray image classification in the presence of noisy labels

Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition

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