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- W4378194406 abstract "Breast cancer is the leading reason for cancer-related fatality among women. Timely diagnosis can improve the survival rate of women suffering from breast cancer. Thermography is a non-contact and non-invasive imaging technique extensively used in the field of breast cancer detection. Thermography is studied as an efficient screening tool capable to detect breast cancer earlier than the traditional mammography methods. The computer-aided system can escalate the rate of diagnosis, treatment, and survival of the patient. In recent times, Vision Transformer (ViT), which is developed on self-attention within image patches, exhibited a great future to be a replacement for CNNs. In this analysis, ViT is utilized to classify breast thermal images for the first time using two different ViT models. The results are presented as classification accuracy and Area under the Curve (AUC) metrics. The results indicate that the ViT models show resembling efficiency with or even better than the CNNs for breast thermal image classification." @default.
- W4378194406 created "2023-05-26" @default.
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- W4378194406 date "2023-01-01" @default.
- W4378194406 modified "2023-09-26" @default.
- W4378194406 title "Vision Transformers for Breast Cancer Classification from Thermal Images" @default.
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- W4378194406 doi "https://doi.org/10.1007/978-981-99-0236-1_13" @default.
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