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Eshraghi-Arani, Malihe, Dehghani-Bidgoli, Zohreh. (1399). Raman Spectroscopy-based Breast Cancer Detection Using Self-Constructing Neural Networks. , 18(2), 89-95. doi: 10.22038/ijmp.2020.44367.1678
Malihe Eshraghi-Arani; Zohreh Dehghani-Bidgoli. "Raman Spectroscopy-based Breast Cancer Detection Using Self-Constructing Neural Networks". , 18, 2, 1399, 89-95. doi: 10.22038/ijmp.2020.44367.1678
Eshraghi-Arani, Malihe, Dehghani-Bidgoli, Zohreh. (1399). 'Raman Spectroscopy-based Breast Cancer Detection Using Self-Constructing Neural Networks', , 18(2), pp. 89-95. doi: 10.22038/ijmp.2020.44367.1678
Eshraghi-Arani, Malihe, Dehghani-Bidgoli, Zohreh. Raman Spectroscopy-based Breast Cancer Detection Using Self-Constructing Neural Networks. , 1399; 18(2): 89-95. doi: 10.22038/ijmp.2020.44367.1678

Raman Spectroscopy-based Breast Cancer Detection Using Self-Constructing Neural Networks

Iranian Journal of Medical Physics
مقاله 2، دوره 18، شماره 2، خرداد و تیر 2021، صفحه 89-95    اصل مقاله (1.13 M)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22038/ijmp.2020.44367.1678
نویسندگان
Malihe Eshraghi-Arani1؛ Zohreh Dehghani-Bidgoli* 2
1Department of Computer Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran
2Department of Biomedical Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran
چکیده
Introduction: Accurate and early diagnosis of cancer is an important issue in modern healthcare systems. Raman spectroscopy, as a non-invasive optical technique for evaluating intact tissues at a molecular level, has attracted the researchers’ attention. Despite recent advances, efforts are still being made to improve the sensitivity and specificity of Raman spectroscopy-based cancer detection. The present study aimed to identify three classes of breast tissues, that is, normal tissues, benign lesions, and cancer tissues, using an artificial neural network (ANN).
Material and Methods: To improve the ANN discrimination power, a novel topologically optimized ANN, known as self-constructing neural network (SCNN), was developed in this study. The ant colony optimization algorithm was applied to optimize the topology of the network. The results of SCNN were compared with the conventional ANN, that is, multilayer perceptron (MLP).
Results: Based on the results, the developed SCNN showed a classification accuracy of 95%.
Conclusion: In this study, a novel neural network (SCNN) was proposed, which was topologically optimized to improve the discrimination power of ANNs. The SCNN accuracy was determined to be 95% in Raman spectroscopy-based breast cancer diagnosis.
کلیدواژه‌ها
Artificial Neural Network Multilayer Perceptron Self؛ Constructing Neural Network Raman Spectroscopy Breast Cancer
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