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| http://dx.doi.org/10.7860/JCDR/2016/18009.8161 | DOI Listing |
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An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition.
Nutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFComparative readability of information on different treatment options for breast cancer, based on WeChat public accounts.
PLoS One
January 2025
School of Nursing, Anhui Medical University, Hefei, Anhui, China.
Objective: To evaluate and compare the readability of information on different treatment options for breast cancer from WeChat public accounts, propose targeted improvement strategies based on the evaluation of the results of the various treatment options, and provide a reference for producers of WeChat public accounts from which to write highly readable information regarding breast cancer treatment options.
Methods: With "breast cancer" as keywords in April 2021, searches were implemented on Sogou WeChat website (https://weixin.sogou.
Cancer is a condition in which cells in the body grow uncontrollably, often forming tumours and potentially spreading to various areas of the body. Cancer is a hazardous medical case in medical history analysis. Every year, many people die of cancer at an early stage.
View Article and Find Full Text PDFDeep learning-based MVIT-MLKA model for accurate classification of pancreatic lesions: a multicenter retrospective cohort study.
Radiol Med
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Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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Smartphone application-based augmented reality for pre-clinical dental implant placement training: a pilot study.
Oral Maxillofac Surg
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Research Center for Digital Technologies in Dentistry and CAD/CAM, Department of Dentistry, Faculty of Medicine and Dentistry, Danube Private University, Steiner Landstraße 123, Krems an der Donau, 3500, Austria.
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