The classification of brain tumors with traditional deep learning methods is challenging due to its
lack of interpretability, computational complexity and its limited classification ability for
multimodal medical image data. The influence of clinical diagnosis can impede early detection of
tumors. The regard, the current Explainable Quantum-Driven Pattern Recognition framework with
Hybrid Deep Learning Networks for Multimodal classification of brain tumors. The proposed
model utilizes feature optimization, inspired by quantum computing, with deep neural architectures,
enhancing feature extraction and classification from multimodal MRI datasets. Furthermore,
mechanisms of Explainable Artificial Intelligence are introduced to improve the transparency of
the models and offer explainable decision support to the healthcare workers. The hybrid structure
is able to learn complex tumor patterns while minimizing information loss and enhancing the
generalization performance. Experimental evaluations were performed using multimodal brain
tumor MRI data and various types of tumors. The proposed method performed better compared to
various other classification models with an accuracy of 98.5%, a precision of 97.8%, a recall of
98.1%, and an F1-score of 98.0%. The deep networks coupled with quantum-inspired learning
significantly improves clinical interpretability, diagnosis accuracy and calculation speed. The
framework offers great benefits for the intelligent healthcare system, including the capability of
rapid and accurate diagnosis of brain tumors, and the ability to provide clear and transparent
information about the condition. The framework could be extremely beneficial for smart health
systems, as it can assist in accelerating the pace, precision, and transparency of brain tumor
diagnosis, enhancing treatment results, and streamlining treatment planning.