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• W4386504089 abstract "Through accurate proliferation rate quantification, an appropriate treatment for breast cancer may be devised. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual evaluation may lead to high costs and ineffectiveness because diagnosis depends on the facility and the pathologists’ insights and experiences. A convolutional neural network is considered feasible as a computer-based observer to improve clinicians' capacity in grading breast cancer. Therefore, this study propose a novel scheme for automatic breast cancer malignancy grading from invasive ductal carcinoma. The proposed classifiers implement multistage transfer learning incorporating domain and histopathological transformations. Domain adaptation using pre-trained models, such as InceptionResNetV2, InceptionV3, NASNet-Large, ResNet50, ResNet101, VGG19, and Xception, was applied to classify the 40 × magnification BreaKHis dataset into eight classes. Then, the best models, in this study, InceptionV3 and Xception, which contain the domain and histopathology pre-trained weights, were used to categorize the Databiox database into grades 1, 2, or 3. To provide a comprehensive report, this study offers a patchless automated grading system for magnification-dependent and magnification-independent classifications. With an overall accuracy of 90.17 % ± 3.08 % – 97.67 % ± 1.09 % and F 1 -score of 0.9013 – 0.9760 for magnification-dependent classification, the classifiers in this work achieved outstanding performance. The proposed approach could be used for breast cancer grading systems in clinical settings." @default.
• W4386504089 created "2023-09-08" @default.
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• W4386504089 date "2023-09-01" @default.
• W4386504089 modified "2023-10-16" @default.
• W4386504089 title "Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System" @default.
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• W4386504089 doi "https://doi.org/10.1016/j.ajpath.2023.07.007" @default.
• W4386504089 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37673327" @default.
• W4386504089 hasPublicationYear "2023" @default.
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