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RISS 인기검색어
- 검색키워드
- 저자 : Kim Gaik Tay (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models
Audrey K. C. Huong,Kim Gaik Tay,Xavier T. I. Ngu 대한의료정보학회 2021 Healthcare Informatics Research Vol.27 No.4
Objectives: Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network(CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores asimplified system using combined binary coding for a five-class version of this problem. Methods: This system extracted featuresfrom transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binarysub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction. Results: Despitethe superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both theVGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigationalso showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificityranging from 93% to 100%. Conclusions: We believe that the AlexNet-SVM model can be conveniently applied for clinicaluse. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well asan appropriate selection of experimental design to improve the efficiency of Pap smear image classification.
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Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods
Waheed Ali Laghari,Audrey Huong,Kim Gaik Tay,Chang Choon Chew 대한의료정보학회 2023 Healthcare Informatics Research Vol.29 No.2
Objectives: Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasetswith poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automaticsegmentation method (HHM) for this classification problem. Methods: Manual segmentation involved selecting a region-ofinterest(ROI) in images from the Bosphorus dataset to generate ground truth data. The HHM combined histogram equalizationand morphological and thresholding-based algorithms to localize veins from hand images. The data were divided intotraining, validation, and testing sets with an 8:1:1 ratio before training AlexNet. We considered three image augmentationstrategies to enlarge our training sets. The best training hyperparameters were found using the manually segmented dataset. Results: We obtained a good test accuracy (91.5%) using the model trained with manually segmented images. The HHMmethod showed slightly inferior performance (76.5%). Considerable improvement was observed in the test accuracy of themodel trained with the inclusion of automatically segmented and augmented images (84%), with low false acceptance andfalse rejection rates (0.00035% and 0.095%, respectively). A comparison with past studies further demonstrated the competitivenessof our technique. Conclusions: Our technique can be feasible for extracting the ROI in DHV images. This strategyprovides higher consistency and greater efficiency than the manual approach.
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