AN EFFICIENT ALGORITHM FOR AUTOMATIC TUMOR DETECTION IN CONTRAST ENHANCED BREAST MRI BY USING ARTIFICIAL NEURAL NETWORK (NEUBREA)
| Autor: | Rahmi Cubuk, Bülent Bayram, Burcu Narin, G. Çiğdem Çavdaroğlu, Hilmi K. Koca, Levent Celik, Ugur Acar |
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| Přispěvatelé: | Maltepe Üniversitesi |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Computer science
education Image processing Computer aided detection Machine learning computer.software_genre medical image processing Benign tumor Breast cancer breast cancer Artificial Intelligence medicine Breast MRI Artificial neural network medicine.diagnostic_test business.industry General Neuroscience Contrast (statistics) Pattern recognition medicine.disease Backpropagation Tumor detection Hardware and Architecture Artificial intelligence business ANN computer Software |
| Popis: | WOS: 000328097600007 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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