AN EFFICIENT ALGORITHM FOR AUTOMATIC TUMOR DETECTION IN CONTRAST ENHANCED BREAST MRI BY USING ARTIFICIAL NEURAL NETWORK (NEUBREA) - Bayram, Bulent; Koca, Hilmi K.; Narin, Burcu; Cavdaroglu, G. Cigdem; Celik, Levent; Acar, Ugur; Cubuk, Rahmi (ACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCE, 2013)
The advances in image processing technology contribute to the interpretation of medical images and early diagnosis. Moreover various studies can be found in medical journals dedicated to Artificial Neural Networks (ANN). In the presented study, a method was developed to learn and detect benign and malignant tumor types in contrast-enhanced breast magnetic resonance images (MRI). The backpropagation algorithm was taken as the ANN learning algorithm. The algorithm (NEUBREA) was developed in C# programming language by using Fast Artificial Neural Network Library (FANN). Having been diagnosed by radiologists, 7 cases of malignant tumor, 8 cases of benign tumor, and 3 normal cases were used as a training set. The results were tested on 34 cases that had been diagnosed by radiologists. After the comparison of the results, the overall accuracy of algorithm was defined as 92%.
Keywords: Computer aided detection; medical image processing; breast cancer; ANN; COMPUTER-AIDED DIAGNOSIS; CANCER; CLASSIFICATION; SEGMENTATION; MAMMOGRAPHY; LESIONS; Computer Science