Data Augmentation Based on Generative Adversarial Networks to Improve Stage Classification of Chronic Kidney Disease

Liao, Yun-Te; Lee, Chien-Hung; Chen, Kuo-Su; Chen, Chie-Pein; Pai, Tun-Wen

doi:10.3390/app12010352

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…The ultrasound kidney images are obtained from different locations [18][19][20]. These data sets consist of kidney images with the types of normal, stone, cyst and tumor.…”

Section: Resultsmentioning

confidence: 99%

An Optimized Transfer Learning Model Based Kidney Stone Classification

Mahalakshmi¹

2023

Computer Systems Science and Engineering

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The kidney is an important organ of humans to purify the blood. The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood. Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases. Early prediction of kidney stones is compulsory for timely treatment. Image processingbased diagnosis approaches provide a greater success rate than other detection approaches. In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL). The Deep Convolutional Neural Network (DCNN) models of DenseNet169, MobileNetv2 and GoogleNet applied for classification. The combined classification results are processed by ensemble learning to increase classification performance. The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer (GTO). The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.

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“…The ultrasound kidney images are obtained from different locations [18][19][20]. These data sets consist of kidney images with the types of normal, stone, cyst and tumor.…”

Section: Resultsmentioning

confidence: 99%

An Optimized Transfer Learning Model Based Kidney Stone Classification

Mahalakshmi¹

2023

Computer Systems Science and Engineering

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“…AP50 value refers to the closed area of the precision and recall curve when the IOU threshold is 0.5. The calculation formulas of precision, recall, AP50 and mAP50 are shown as Equations ( 7)- (10).…”

Section: Experimental Evaluation Criteriamentioning

confidence: 99%

“…Since its introduction, deep learning has been widely used in related research, such as in the medical field [9][10][11][12][13], various types of target recognition [14][15][16] and natural language processing [17], and has achieved quite good results in these fields. Therefore, using the deep learning method to detect fabric defects will result in more accurate detection results.…”

Section: Introductionmentioning

confidence: 99%

Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO

Yang

Wang

et al. 2022

Applied Sciences

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Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant position in the field of target detection. However, detecting small-scale objects, such as tiny targets in fabric defects, is still a very challenging task for the YOLOv4 network. To address this challenge, this paper proposed an improved YOLOv4 target detection algorithm: using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection, integrating a convolutional block attention module into the backbone feature extraction network, and innovatively replacing the CIOU loss function with the CEIOU loss function to achieve accurate classification and localization of defects. Experimental results show that compared with the original YOLOv4 algorithm, the detection accuracy of the improved YOLOv4 algorithm for tiny targets has been greatly increased, the AP value of tiny target detection has increased by 12%, and the overall mean average precision (mAP) has increased by 3%. The prediction results of the proposed algorithm can provide enterprises with more accurate defect positioning, reduce the defect rate of fabric products, and improve their economic effect.

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“…Liao et al 34 recognized data augmentation with generative adversarial networks by enhancing CKD classification. An auxiliary analysis system with the DL technique can be provided to recognize CKD classification stages for renal ultra‐sound imageries.…”

Section: Related Workmentioning

confidence: 99%

“…Liao et al 34 An analytic CKD prediction method was projected by Hosseinzadeh et al 37 to predict early-stage severity. To minimize time consumption, suitable features were selected based on clinical observations.…”

Section: Related Workmentioning

confidence: 99%

Ebola deep wavelet extreme learning machine based chronic kidney disease prediction on the internet of medical things platform

Reddy

Vydeki

2022

Concurrency and Computation

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Summary Various significant methodologies have been developed in classifying chronic kidney disease (CKD). But still, there emerge certain drawbacks, including high storage space requirements, increased diagnosis time, high cost of computation, and degraded accuracy. Hence in the proposed research work, Ebola deep wavelet extreme learning machine (EDWELM) is proposed for the precise classification of CKD and non‐CKD. Initially, the accessed data are preprocessed by transforming categorical to numerical values and replacing missing values with median to remove unwanted distortions. In the feature selection process, the hybrid methodology of Darts game and Battle royale optimization called Darts Battle game optimizer is carried out to choose the most discriminative features for improving classification accuracy. The final step undertaken is the classification process, which is one of the important data mining applications for distinguishing the data classes. In the proposed EDWELM classification method, ELM based autoencoder, wavelet neural network, and Ebola optimization search algorithm are carried out for effective CKD classification. From the CKD dataset, the performance is analyzed to various terms like accuracy, F1 score, precision, recall, kappa, and balanced score. The accuracy of 99.83% is attained by the proposed EDWELM classification method, which is comparatively better than the existing approaches.

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Data Augmentation Based on Generative Adversarial Networks to Improve Stage Classification of Chronic Kidney Disease

Cited by 9 publications

References 26 publications

An Optimized Transfer Learning Model Based Kidney Stone Classification

An Optimized Transfer Learning Model Based Kidney Stone Classification

Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO

Ebola deep wavelet extreme learning machine based chronic kidney disease prediction on the internet of medical things platform

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