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- W4200577603 abstract "Aim: Breast cancer is the leading cause of death among women around the world. Because of its low cost and the fact that it does not emit hazardous radiation, infrared thermography has emerged as a viable approach for diagnosing the condition in young women. This study aims to create a computer-aided diagnostic system that can process thermographic breast cancer images and classify breast cancer with pre-trained networks in order to use thermography as a diagnostic method.
 Materials and Methods: In this study, an open-access data set consisting of thermographic breast cancer images was used for diagnostic purposes. The data set consists of 179 healthy images and 101 images from patients. The images were converted from .txt format to .jpeg format. The data set is acquired from http://visual.ic.uff.br/dmi/. In this study, various pre-trained networks were used to reduce the training time. Different metrics were employed to assess the performance of the models.
 Results: The images obtained during the modeling phase were used to display both breasts in the image without distinguishing the right and left breasts, that is, without fragmenting the images. According to the results of the different pre-trained network models after the data preprocessing stages, the best classification performance was achieved for the ResNet50V2 model with an accuracy value of 0.996.
 Conclusion: In this study, a computer-aided diagnosis system was created by developing an interface for breast cancer classification from thermographic images in addition to experimental findings. The web software based on the proposed models has provided promising predictions of breast cancer from thermographic images. The developed software can help medical and other healthcare professionals easily spot breast cancer." @default.
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- W4200577603 date "2021-12-30" @default.
- W4200577603 modified "2023-10-16" @default.
- W4200577603 title "BREAST CANCER DIAGNOSIS BASED ON THERMOGRAPHY IMAGES USING PRE-TRAINED NETWORKS" @default.
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- W4200577603 doi "https://doi.org/10.52876/jcs.990948" @default.
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