Breast cancer is the main cause of all female cancer deaths worldwide. Because of the lack of early symptoms, the early detection of breast cancer becomes challenging. The detection is performed by screening techniques in organized preventive examinations. A promising imaging technology that can detect bio-molecular alterations and can support the screening technologies by enhancing their low sensitivity, is nonlinear multimodal imaging. To detect these bio-molecular alterations machine learning algorithms are utilized. Our analysis starts by preprocessing the images and comparing them to the pathological diagnosis. We trained two classification models utilizing the deep convolutional neural network ResNet50. This network was either used as feature extractor or to be finetuned. Beside these two classification approaches, two data validation techniques were investigated: the leave-one-patient-out cross-validation (LOPO-CV) and the training-test validation. The best reported result of breast cancer detection was introduced by the finetuned ResNet50 network and LOPO-CV accounting to 86.23% mean-sensitivity.