Fine-tuning the hyperparameters of pre-trained models for solving multiclass classification problems
Автор: Kaibassova Dinara, Nurtay Margulan, Tau Ardak, Kissina Mira
Журнал: Компьютерная оптика @computer-optics
Рубрика: Численные методы и анализ данных
Статья в выпуске: 6 т.46, 2022 года.
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This study is devoted to the application of fine-tuning methods for Transfer Learning models to solve the multiclass image classification problem using the medical X-ray images. To achieve this goal, the structural features of such pre-trained models as VGG-19, ResNet-50, InceptionV3 were studied. For these models, the following fine-tuning methods were used: unfreezing the last convolutional layer and updating its weights, selecting the learning rate and optimizer. As a dataset chest X-Ray images of the Society for Imaging Informatics in Medicine (SIIM), as the leading healthcare organization in its field, in partnership with the Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), the Valencian Region Medical ImageBank (BIMCV) ) and the Radiological Society of North America (RSNA) were used. Thus, the results of the experiments carried out illustrated that the pre-trained models with their subsequent tuning are excellent for solving the problem of multiclass classification in the field of medical image processing. It should be noted that ResNet-50 based model showed the best result with 82.74 % accuracy. Results obtained for all models are reflected in the corresponding tables.
Multiclass classification, transfer learning, fine-tuning, cnn, image augmentation, x-ray
Короткий адрес: https://sciup.org/140296245
IDR: 140296245 | DOI: 10.18287/2412-6179-CO-1078
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