A Bayesian Backed DeepLabV3+ Customized Hybrid-Depth Framework for Brain Tumor Segmentation and Classification

Efficient diagnosis, treatment planning, and patient care requires that brain tumors in MRI images must be accurately segmented and classified. Many deep learning practices are used to perform these tasks. However, prominent performance gaps like insufficient hierarchical feature extraction, lesser-known biological delineation of tumor and limited uncertainty modeling exist. These gaps specifically occur while dealing with multi-modal MRI data and heterogenous tumor shapes. These gaps are addressed by introducing a Bayesian based DeepLabV3+ architecture. It quantifies aleatoric and epistemic uncertainty of segmentation. Hence, it provides improved reliability on clinical decisions. The selection of architecture is backed by the promising ability of Bayesian inference and preservation of multi-scale spatial context which is suited for complex morphology of brain tumors. The architecture is tested on BraTS23 and Figshare brain tumor datasets which has multi-modal annotations of tumor sub regions and consist of multiple tumor recognitions respectively. It performed remarkably to properly manage a range of tumor forms and sizes by achieving Dice scores of 95.86% on BraTS23 and 92.80% on the Figshare dataset for segmentation tasks, which its statistical significance is proven through several cross-validation trials (p< 0.05) and superior performance compared with leading approaches reported in recent literature. Furthermore, a unique Hybrid-Depth classification framework is developed, which merges hierarchical and coarse-level feature extraction using dual-branch processing and self-attention. it is a design choice that is justified by the requirement to explicitly characterize cross-modal interdependence and mitigate feature redundancy. The framework performed effectively to classify brain tumor MRI modalities (Flair, T1, T2 and T1ce) and tumor type classes (i.e. gliomas, meningiomas and pituitary). The hierarchical and coarser feature extraction for classification task is done gracefully. The proposed architecture achieved 98.80% and 95.40% accuracy for the BraTS23 and Figshare brain tumor datasets, respectively. The work demonstrates its uniqueness and practical usefulness by defining the Hybrid-Depth mechanism and detailing its role in increasing discriminative representational learning. The framework’s performance demonstrates both its potential for practical use in automated brain tumor diagnosis and its superiority over current approaches. The proposed framework provided a major step toward increasing diagnostic efficiency, accuracy and assisting healthcare providers in providing the best possible patient care. In a nutshell, the proposed Bayesian-DeepLabV3+ and Hybrid-Depth frameworks jointly enhance the field by bridging methodological gaps in quantification of uncertainty, selection of dataset-driven architecture, and multi-modal feature learning. Hence, the approach offers a robust AI pipeline for automated brain tumor diagnosis.