Explainable Quantum-Driven Pattern Recognition for Multimodal Brain Tumor Classification Using Hybrid Deep Learning Networks

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.