STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network - Nafiul Hasan; Md. Masud Rana; Md Mahmudul Hasan; AKM Azad; Dil Afroz; Md Mostafizur Rahman Komol; Mousumi Aktar; Mohammad Ali Moni (Engineering Reports)
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.