  Cancer disease is one of the leading causes of death all over the world.
Breast cancer, which is a common cancer disease especially in women, is quite
common. The most important tool used for early detection of this cancer type,
which requires a long process to establish a definitive diagnosis, is
histopathological images taken by biopsy. These obtained images are examined by
pathologists and a definitive diagnosis is made. It is quite common to detect
this process with the help of a computer. Detection of benign or malignant
tumors, especially by using data with different magnification rates, takes
place in the literature. In this study, two different balanced and unbalanced
study groups have been formed by using the histopathological data in the
BreakHis data set. We have examined how the performances of balanced and
unbalanced data sets change in detecting tumor type. In conclusion, in the
study performed using the InceptionV3 convolution neural network model, 93.55%
accuracy, 99.19% recall and 87.10% specificity values have been obtained for
balanced data, while 89.75% accuracy, 82.89% recall and 91.51% specificity
values have been obtained for unbalanced data. According to the results
obtained in two different studies, the balance of the data increases the
overall performance as well as the detection performance of both benign and
malignant tumors. It can be said that the model trained with the help of data
sets created in a balanced way will give pathology specialists higher and
accurate results.
