Purpose: Breast cancer is among the leading causes of cancer death among women. The occurrence of breast cancer is similar both in developed countries and in underdeveloped and developing nations, although mortality is higher in underdeveloped countries due to late detection. Even though mammography is the most used technique for the differential diagnosis of breast cancer, breast thermography can be used as a complementary technique, more accurate than self-examination but accurate enough to guide the use of a mammogram. However, thermal imaging is still difficult for radiologists to understand. Machine learning can help improve this scenario. Deep Wavelet Neural Networks are convolutional neural networks that do not necessarily learn, as they may have predefined filter banks as their neurons. Methods: In this work, we propose a deep hybrid architecture to support breast thermography imaging diagnosis based on five-layer Deep-Wavelet Neural Networks, to extract attributes of regions of interest from mammograms, and linear kernel support vector machines for final classification.
Results: Classical classifiers such as Bayesian classifiers, single hidden layer multilayer perceptrons, decision trees, Random Forests, and support vector machines were tested. The results showed that it is possible to detect and classify injuries with an average accuracy of 99% and an average kappa of 0.99, employing a 5-layer deep-wavelet network and a linear kernel support vector machine as the final classifier.
Conclusion: Using a deep neural network with prefixed weights from the Wavelets Transform filterbank, it was possible to extract attributes and thus take the problem to a universe where it can be solved with relatively simple decision boundaries like those composed by linear kernel support vector machines. This shows that these new deep networks can be important in building complete solutions to improve breast thermography imaging results to support clinical diagnosis.