Raman Spectroscopy-based Breast Cancer Detection Using Self-Constructing Neural Networks
Publish place: Iranian Journal of Medical Physics، Vol: 18، Issue: 2
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
View: 339
This Paper With 7 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJMP-18-2_002
تاریخ نمایه سازی: 6 اردیبهشت 1400
Abstract:
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 ۹۵%. 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 ۹۵% in Raman spectroscopy-based breast cancer diagnosis.
Keywords:
Artificial Neural Network Multilayer Perceptron Self , Constructing Neural Network Raman Spectroscopy Breast Cancer
Authors
Malihe Eshraghi-Arani
Department of Computer Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran
Zohreh Dehghani-Bidgoli
Department of Biomedical Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :