Combining deep-wavelet neural networks and support-vector machines to classify breast lesions in thermography images

Abstract

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 * Corresponding author: Wellington Pinheiro dos Santos, ORCID 0000-0003-2558-6602 DWNN to classify breast lesions in thermographies 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.

Evaluation of molecular docking by deep learning and Random Forests: A hybrid approach based on pseudo-convolutions

“…These models have demonstrated impressive performance in support diagnosis applications based on biomedical images, signals and clinical parameters, like in breast cancer, Covid-19, mental disorders, and in neurodegenerative diseases like Alzheimer's and Parkinson's (Azevedo et al, 2015;Barbosa et al, 2022;F.R. Cordeiro, Santos, & Silva-Filho, 2016b;de Lima, da Silva-Filho, & dos Santos, 2016;De Oliveira et al, 2020;de Santana, de Freitas Barbosa, de Cássia Fernandes de Lima, & dos Santos, 2022;de Santana et al, 2018; Santos Lucas e Silva, dos Santos, de Lima, & Initiative, 2021;Espinola, Gomes, Pereira, & dos Santos, 2021a, 2021bFonseca et al, 2022;Gomes et al, 2020;Gomes, de Santana, Masood, de Lima, & dos Santos, 2023;Gomes, Rodrigues, & dos Santos, 2022;Santana et al, 2018;Shirahige et al, 2022;Wanderley Espinola, Gomes, Mônica Silva Pereira, & dos Santos, 2022). Such models have been used successfully in accurately predicting protein-protein binding affinities.…”