Machine Learning Empowered a Graphical User Interface on Native Fluorescence to Predict Breast Cancer Article Swipe
Ashwini Amin
,
Mallika Priya
,
Jackson Rodrigues
,
Shimul Biswas
,
Subhash Chandra
,
Stanley Mathew
,
Satadru Ray
,
Bola Sadashiva Satish Rao
,
Krishna Kishore Mahato
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acsomega.4c11669
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acsomega.4c11669
Breast cancer poses a significant global health challenge, requiring improved diagnostic solutions for its timely intervention and treatment. Real-time diagnostic approaches in current practice offer promising avenues for early detection. However, these techniques often lack specificity, necessitating the development of robust diagnostic tools for real-time applications. In the current study, fluorescence spectroscopy is integrated with machine learning algorithms, and a graphical user interface (GUI) is developed for rapid breast cancer prediction. This study records 206 native fluorescence spectra, 103 spectra each from 31 normal and 31 malignant breast tissues using 325 nm excitation, followed by discrimination analysis using different machine learning algorithms, including backpropagation artificial neural network (BP-ANN), support vector machine (SVM), and Naïve Bayes (NB). Comparative analysis reveals that SVM in combination with a polynomial kernel demonstrated the superior performance of accuracy (98.78%), sensitivity (100%), specificity (97.56%), and precision (97.62%), among others. Furthermore, the in-house developed GUI applied to the current data showed the possibility of real-time prediction of pathological breast tissues, facilitating standalone applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acsomega.4c11669
- OA Status
- gold
- Cited By
- 1
- References
- 50
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4410347758Canonical identifier for this work in OpenAlex
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https://doi.org/10.1021/acsomega.4c11669Digital Object Identifier
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Machine Learning Empowered a Graphical User Interface on Native Fluorescence to Predict Breast CancerWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-14Full publication date if available
- Authors
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Ashwini Amin, Mallika Priya, Jackson Rodrigues, Shimul Biswas, Subhash Chandra, Stanley Mathew, Satadru Ray, Bola Sadashiva Satish Rao, Krishna Kishore MahatoList of authors in order
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https://doi.org/10.1021/acsomega.4c11669Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1021/acsomega.4c11669Direct OA link when available
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Computer science, Interface (matter), Graphical user interface, Human–computer interaction, Breast cancer, Fluorescence, Cancer, Artificial intelligence, Machine learning, Medicine, Programming language, Operating system, Optics, Internal medicine, Physics, Bubble, Maximum bubble pressure methodTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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