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Title |
Automated Classification of Breast Cancer Histology Images Using Deep Learning Based Convolutional Neural Networks |
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Author |
Majid Ali Nawaz, Adel A. Sewissy, Taysir Hassan A. Soliman |
| Citation |
Vol. 18 No. 4 pp. 152-160 |
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Abstract |
Automated classification of cancers using histopathological images is a challenging task of accurate detection of tumor sub-types. In this paper, we applied fine-tuned pre-trained deep neural networks classified on BreakHis datasets on eight distinct classes for benign has four sub-classes (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) malignant has four sub-classes (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma) all together on difference model on Inception (V1,V2) and ResNet V1 50. The confusion matrix showing high accuracy value 95% with less error rate 0.011 . |
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Keywords |
Medical imaging, Computer-aided diagnosis (CAD), Deep Learning, Medical image processing, Convolution Neural Network. |
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