Abstract:

Breast cancer is one of the leading diseases among diseases that can cause death in women. Studies have shown that early detection of breast cancer can reduce mortality rates. The mammogram images used in the diagnosis of breast cancer are examined by radiologists. Investigations take a long time and sometimes erroneous results can be obtained. Studies using artificial intelligence methods are very important for the diagnosis of breast cancer in the early stages. It has also been observed that the diagnoses obtained with mammogram images are better than the diagnosis of medical experts. With the developing technology, many different deep learning models are used in the diagnosis of the disease. In this study, the Inception-ResNet-V2 deep learning model is proposed for the diagnosis of breast cancer. The proposed deep learning model is a hybrid architecture of Inception and ResNet models and has effectively improved classification and diagnosis performance. The proposed deep learning architecture consists of three stages, namely preprocessing, classification, and performance evaluation, respectively. With the proposed model, 96.21% accuracy, 97.48% recall, 98.18% precision, 97.83% F-score, 98.00% area under the curve, and 0.83 cohen kappa performance values were obtained. The obtained results prove that the proposed model performs better in breast cancer diagnosis when compared with the results obtained from other deep learning architectures used in the benchmarking stage in the study.

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