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تسجيل مستخدم جديدA New Gastric Histopathology Subsize Image Database (GasHisSDB) for Classification Algorithm Test: from Linear Regression to Visual Transformer
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GasHisSDB is a New Gastric Histopathology Subsize Image Database with a total of 245196 images. GasHisSDB is divided into 160*160 pixels sub-database, 120*120 pixels sub-database and 80*80 pixels sub-database. GasHisSDB is made to realize the function of valuating image classification. In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation. Seven classical machine learning classifiers, three CNN classifiers and a novel transformer-based classifier are selected for testing on image classification tasks. GasHisSDB is available at the URL:https://github.com/NEUhwm/GasHisSDB.git.
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Existing deep learning methods for diagnosis of gastric cancer commonly use convolutional neural network. Recently, the Visual Transformer has attracted great attention because of its performance and efficiency, but its applications are mostly in the
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