This paper proposes a concept of dielectric characterization of low-volume liquid samples using the coupling coefficient of filters. The concept is validated through a two-pole substrate integrated waveguide filter in which the liquid under test is mounted on the coupling section between the two resonators. Unlike the conventional resonator perturbation method reported many times in the literature, this technique uses the coupling coefficient for sensing. The liquid sample is collected in a capillary tube and carefully positioned on the coupling section of the filter; the coupling coefficient of the two resonators varies compared to the relative permittivity of the sample; thus, an empirical model is established. The proposed sensor has been tested to compute the permittivity of different alcohols. Binary solutions of ethanol and water have also been characterized to calculate the volume ratio and relative permittivity as a proof-of-concept. The obtained results show that the proposed sensing technique is capable of characterizing a low quantity of liquids (≈44 µL) with good accuracy, and a worst case measured error of only 6.8% is noted. The ease of integration with other circuitry, low cost, reusability with no deterioration, and adaptability of the proposed sensor makes it a suitable choice for the chemical as well as for the pharmaceutical industry.

Currently, advances in healthcare technologies are transforming medical diagnostics, particularly for data-driven disease detection. Acute lymphoblastic leukemia is a common and life-threatening blood cancer, especially prevalent in children. Although existing artificial intelligence-based diagnostic models have demonstrated promising accuracy, their black-box nature limits transparency, explainability, and clinical trust. Moreover, most explainable artificial intelligence approaches for leukemia diagnosis rely primarily on qualitative visual explanations and lack a unified quantitative mechanism to measure trust. This study presents a novel, trust-centered explainable deep-learning framework for automated acute lymphoblastic leukemia detection using 153 publicly available microscopic blood smear images and multiple transfer-learning models. Among the evaluated architectures, the fine-tuned EfficientNetB4 achieved high diagnostic performance, attaining an accuracy of 98.31%, and was selected as the base model of the proposed framework. Beyond diagnostic accuracy, this work introduces a novel unified interpretability score that quantitatively assesses model trustworthiness by integrating diagnostic performance, explanation robustness (evaluated using deletion and stability metrics), and clinician trust feedback into a single reliability measure. This quantitative trust formulation represents a distinct advancement over existing studies based on explainable artificial intelligence, which typically rely on isolated or qualitative explainability assessments. The framework further enhances transparency through visual and textual explanations generated using standard post hoc explainable methods and advanced fusion heatmaps, while human-centric evaluation is conducted using two clinician-based trust scales. Overall, this study provides a unified and transparent framework that jointly evaluates accuracy, explainability, and clinician trust, representing a step towards the clinical adoption of trustworthy artificial intelligence driven leukemia diagnostic systems.