期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：10
DOI：10.14569/IJACSA.2021.0121054
语种：English
出版社：Science and Information Society (SAI)
摘要：Now-a-days, breast cancer is the most crucial problem amongst men and women. A massive number of people are invaded with breast cancer all over the world. An early diagnosis can help to save lives with proper treatment. Recently, computer-aided diagnosis is becoming more popular in medical science as well as in cancer cell identification. Deep learning models achieve excessive attention because of their performance in identifying cancer cells. Mammography is a significant creation for detecting breast cancer. However, due to its complex structure, it is challenging for doctors to identify. This study provides a convolutional neural network (CNN) approach to detecting cancer cells early. Dividing benign and malignant mammography images can significantly improve detection and accuracy levels. The BreakHis 400X dataset is collected from Kaggle and DenseNet-201, NasNet-Large, Inception ResNet-V3, Big Transfer (M-r101x1x1); these architectures show impressive performance. Among them, M-r101x1x1 provides the highest accuracy of 90%. The main priority for this research work is to classify breast cancer with the highest accuracy with selected neural networks. This study can improve the systematic way of early-stage breast cancer detection and help physicians' decision-making.
关键词：Convolutional neural network (CNN); breast cancer; Big Transfer (BiT); densenet-201; NasNet-Large; Inception-Resnet-v3; mammography
