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- W4367011802 abstract "Currently, different methods are available for the purpose of breast cancer classification and detection. Most of these techniques are well appreciated by society and in response to the demand of society, almost every year the different techniques are introduced by different researchers, but it does not satisfy the demand of current requirements. Under such a situation, we are going to propose a new breast cancer classification and detection algorithm using a convolutional approach. This technique starts with the mammogram preprocessing step. It is followed by the convolutional model architecture design step. In the next step, the segregation of the dataset into the training and testing phase is performed. Then, the convolutional model architecture is trained using the training dataset and pre-masked images. After that our proposed algorithm predicts, the breast cancer detection and classification result. We have found that our proposed algorithm can be used for breast tumor detection and classification from mammogram images with the average approximate accuracy of 98.5% and the average approximate F1 score of 0.98. Novel preprocessing steps and modifications in the convolutional architecture make the proposed methodology unique. Due to high performance, novelty, ease of use, our proposed method is useful to develop any mobile or web application in the future." @default.
- W4367011802 created "2023-04-27" @default.
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- W4367011802 date "2023-01-01" @default.
- W4367011802 modified "2023-09-25" @default.
- W4367011802 title "An Unstructured Mammogram Analysis for Feasible Classification and Detection of Breast Cancer Using a Convolutional Approach" @default.
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- W4367011802 doi "https://doi.org/10.1007/978-981-19-5191-6_14" @default.
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