Abstract
One of the best methods for Breast cancer diagnosis is histopathological images from the visual analysis of histology, but pathologist requires lots of experience and training to an accurate diagnosis. Therefore, computer-aided diagnosis (CAD) is an automated and more precise method. Recent developments in computer vision and deep learning (DL), DL based models are popular in analyzing the hematoxylin–eosin (H&E) stained breast cancer digital slides. This paper proposed a deep learning-based framework, called multi-classification of breast histopathological image using Xception: Deep Learning with Depth wise Separable Convolutions model (MCBHIX). Xception based on depthwise separable convolution layer. We trained this network from scratch for binary classes and for multi-classes BreakHis dataset. The accuracy achieved by MCBHIX- 99.01% for binary type and 96.57% for Multiclass.
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Pujari, S.D., Pawar, M.M., Wadekar, M. (2021). Multi-Classification of Breast Histopathological Image Using Xception: Deep Learning with Depthwise Separable Convolutions Model. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_54
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DOI: https://doi.org/10.1007/978-3-030-69921-5_54
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