STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network
ABSTRACT Early‐stage brain tumor detection is critical for improving patient outcomes, optimizing treatment strategies, and enhancing healthcare resource allocation. However, existing state‐of‐the‐art techniques struggle to detect tumors smaller than 5 mm due to their minimal dimensions and complex...
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Wiley
2025-05-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70135 |
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| author | Nafiul Hasan Md. Masud Rana Md Mahmudul Hasan AKM Azad Dil Afroz Md Mostafizur Rahman Komol Mousumi Aktar Mohammad Ali Moni |
| author_facet | Nafiul Hasan Md. Masud Rana Md Mahmudul Hasan AKM Azad Dil Afroz Md Mostafizur Rahman Komol Mousumi Aktar Mohammad Ali Moni |
| author_sort | Nafiul Hasan |
| collection | DOAJ |
| description | ABSTRACT Early‐stage brain tumor detection is critical for improving patient outcomes, optimizing treatment strategies, and enhancing healthcare resource allocation. However, existing state‐of‐the‐art techniques struggle to detect tumors smaller than 5 mm due to their minimal dimensions and complex electromagnetic interactions. This study introduces a machine learning‐based classification approach for early‐stage Astrocytoma tumors (grades I and II) using step‐constant tapered slot antenna (STSA) parameters. By leveraging scattering (S), admittance (Y), and impedance (Z) parameters as input features, an Artificial Neural Network (ANN) achieved a 99.95% classification accuracy for tumors with radii of 3 mm and 5 mm. Among the input features, impedance (Z) was identified as the most significant contributor to classification accuracy, whereas the S‐parameter exhibited the lowest performance at 84.21% accuracy. The proposed methodology was benchmarked against Support Vector Machine (SVM), K‐Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Graph Convolutional Neural Network (GCN), demonstrating superior classification performance across different tumor sizes. Additionally, the system maintained a low Specific Absorption Rate (SAR) of 0.30 W/Kg, reinforcing its suitability for biomedical antenna‐based applications. An ablation study further confirmed that Z22 and Z14 phase components within the impedance matrix were particularly influential, as revealed through Local Interpretable Model‐Agnostic Explanations (LIME), an explainable AI (XAI) technique. The proposed method was evaluated using a publicly available dataset, validating its robustness. These findings highlight the potential of STSA‐based machine learning models for accurate, non‐invasive early‐stage brain tumor classification, enabling cost‐effective, scalable diagnostics. |
| format | Article |
| id | doaj-art-c00e4b1c84134036b15cbe1f97cdfee2 |
| institution | OA Journals |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-c00e4b1c84134036b15cbe1f97cdfee22025-08-20T02:34:10ZengWileyEngineering Reports2577-81962025-05-0175n/an/a10.1002/eng2.70135STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural NetworkNafiul Hasan0Md. Masud Rana1Md Mahmudul Hasan2AKM Azad3Dil Afroz4Md Mostafizur Rahman Komol5Mousumi Aktar6Mohammad Ali Moni7Department of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi BangladeshDepartment of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi BangladeshFaculty of Electrical & Computer Engineering Bangladesh Army University of Engineering & Technology (BAUET) Natore BangladeshDepartment of Mathematics and Statistics Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi ArabiaFaculty of Electrical & Computer Engineering Bangladesh Army University of Engineering & Technology (BAUET) Natore BangladeshSchool of Electrical Engineering & Robotics Queensland University of Technology (QUT) Brisbane Queensland AustraliaDepartment of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi BangladeshArtificial Intelligence And Cyber Futures Institute Charles Sturt University Bathurst NSW AustraliaABSTRACT Early‐stage brain tumor detection is critical for improving patient outcomes, optimizing treatment strategies, and enhancing healthcare resource allocation. However, existing state‐of‐the‐art techniques struggle to detect tumors smaller than 5 mm due to their minimal dimensions and complex electromagnetic interactions. This study introduces a machine learning‐based classification approach for early‐stage Astrocytoma tumors (grades I and II) using step‐constant tapered slot antenna (STSA) parameters. By leveraging scattering (S), admittance (Y), and impedance (Z) parameters as input features, an Artificial Neural Network (ANN) achieved a 99.95% classification accuracy for tumors with radii of 3 mm and 5 mm. Among the input features, impedance (Z) was identified as the most significant contributor to classification accuracy, whereas the S‐parameter exhibited the lowest performance at 84.21% accuracy. The proposed methodology was benchmarked against Support Vector Machine (SVM), K‐Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Graph Convolutional Neural Network (GCN), demonstrating superior classification performance across different tumor sizes. Additionally, the system maintained a low Specific Absorption Rate (SAR) of 0.30 W/Kg, reinforcing its suitability for biomedical antenna‐based applications. An ablation study further confirmed that Z22 and Z14 phase components within the impedance matrix were particularly influential, as revealed through Local Interpretable Model‐Agnostic Explanations (LIME), an explainable AI (XAI) technique. The proposed method was evaluated using a publicly available dataset, validating its robustness. These findings highlight the potential of STSA‐based machine learning models for accurate, non‐invasive early‐stage brain tumor classification, enabling cost‐effective, scalable diagnostics.https://doi.org/10.1002/eng2.70135antennabrain tumorLIMEmachine learningSTSAXAI |
| spellingShingle | Nafiul Hasan Md. Masud Rana Md Mahmudul Hasan AKM Azad Dil Afroz Md Mostafizur Rahman Komol Mousumi Aktar Mohammad Ali Moni STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network Engineering Reports antenna brain tumor LIME machine learning STSA XAI |
| title | STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network |
| title_full | STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network |
| title_fullStr | STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network |
| title_full_unstemmed | STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network |
| title_short | STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network |
| title_sort | stsa based early stage detection of small brain tumors using neural network |
| topic | antenna brain tumor LIME machine learning STSA XAI |
| url | https://doi.org/10.1002/eng2.70135 |
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