The diagnosis of glioma, a complex and often deadly brain tumor, involves extensive medical examinations. Still, accurately grading and classifying gliomas is difficult, as different areas within the same tumor can exhibit varying characteristics. The integration of radiomics, a clinically relevant feature extraction method, with machine learning (ML) is becoming increasingly popular in addressing this issue, but several research gaps persist. To this end, this study proposes a novel deep neural network, RGNN3D, that combines Graph Neural Networks with LSTM layers to precisely grade gliomas in 3D magnetic resonance imaging (MRI) data. To train our proposed model, we meticulously extracted 112 radiomic biomarkers. Utilizing the biomarkers, RGNN3D constructs a graph, channels essential information through its layers, and preserves only pertinent information via its integrated memory cells. The proposed framework attained an accuracy of 98.58%, aligning with the performance of previous state-of-the-art architectures and surpassing prior radiomic-based ML models. We further employed an explainable AI approach (LIME) to highlight the most significant features, assisting radiologists in making more informed decisions. In short, RGNN3D offers a reliable and robust computer-aided solution for potential clinical application in the automated identification of gliomas.

Despite the advancements in quantum convolution or quanvolution, challenges persist in making quanvolution scalable, efficient, and applicable to multi-dimensional data. Existing quanvolutional networks heavily rely on classical layers, with minimal quantum involvement due to inherent limitations in current quanvolution algorithms. Moreover, the application of quanvolution in the domain of 1D data remains largely unexplored. To address these limitations, we propose a new quanvolution algorithm-Quanv1D-capable of processing arbitrary-channel 1D data, handling variable kernel sizes, and generating a customizable number of feature maps, along with a classification network-fully quanvolutional network (FQN)-built solely using Quanv1D layers. Quanv1D is inspired by the classical Conv1D and stands out from the quanvolution literature by being fully trainable, modular, and freely scalable with a self-regularizing feature. To evaluate FQN, we tested it on 20 UEA and UCR time series datasets, both univariate and multivariate, and benchmarked its performance against state-of-the-art convolutional models (both quantum and classical). We found FQN to outperform all compared models in terms of average accuracy while using significantly fewer parameters. Additionally, to assess the viability of FQN on real hardware, we conducted a shot-based analysis across all the datasets to simulate statistical quantum noise and found our model robust and equally efficient.