Leveraging Transfer Learning for High-Accuracy Breast CancerClassification f rom Histopathological Images
Publish place: The 6th International Conference on Electrical Engineering, Computer, Mechanics and Artificial Intelligence
Publish Year: 1403
Type: Conference paper
Language: English
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Document National Code:
EECMAI06_037
Index date: 19 June 2024
Leveraging Transfer Learning for High-Accuracy Breast CancerClassification f rom Histopathological Images abstract
Early detection of breast cancer remains an important global health concern. Inthis paper, we present a novel method for classifying breast cancer usinghistopathological images from the BreakHis dataset at 400X resolution. Weextract high-level features capturing malignancy patterns using VGG19 andDenseNet201. For final classification, these features are concatenated and fed intoan Artificial Neural Network (ANN), which achieves an impressive accuracy of99%. The high accuracy of our methodology demonstrates its potential as aneffective diagnostic tool in the digital pathology era.
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Leveraging Transfer Learning for High-Accuracy Breast CancerClassification f rom Histopathological Images authors
Amir Mohammad Sharafaddini
Department of Computer Science, Shahid Bahonar University of Kerman, Kerman,Box No. ۷۶۱۳۵-۱۳۳, Kerman, Iran.
Najme Mansouri
Department of Computer Science, Shahid Bahonar University of Kerman, Kerman,Box No. ۷۶۱۳۵-۱۳۳, Kerman, Iran
