A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer

using BreakHis data

• Benign/Malignant Classification with 98.8% accuracy.
• Sub-Benign Disease Classification with 95.5% accuracy.
• Sub-Malignant Disease Classification with 92.8% accuracy.

# Typical tf.keras API usage
from breastnet import BreastNet

model = BreastNet(input_shape=..., n_classes=...)
model.compile(...)
history = model.fit(...)

• All training codes and history
• Evaluation Results in Accuracy, F1-Macro, ROC-AUC.. metrics
• • see research folder for details.

M. Togaçar, K.B. Özkurt, B. Ergen et al., BreastNet: A novel ˘
convolutional neural network model through histopathological images for the diagnosis of breast
cancer, Physica A (2019), doi: https://doi.org/10.1016/j.physa.2019.123592 .

If you have any questions about the research, feel free to ask!

E-mail: [email protected]

This project is licensed under the MIT LICENSE - see the LICENSE file for details.