Brain tumor segmentation and classification: A CVAE-UNETR-ResNet50-VGG16 hybrid deep learning approach

This paper introduces a new hybrid DL framework, CVAE-UNETR-ResNet50-VGG16, for accurate brain tumor segmentation and classification from MRI and BraTS2021 scans. The proposed model integrates a Convolutional Variational Autoencoder (CVAE) for synthetic MRI and BraTS2021 data generation, a UNET Transformer (UNETR) for enhanced spatial segmentation through global self-attention, and ResNet50 and VGG16 networks for robust multi-scale feature classification. Moreover, data augmentation technique is proposed based on both CVAE and diffusion technique. Experimental results on the BraTS2021 dataset demonstrate a 2.57 % overall improvement in segmentation and classification performance compared to conventional UNET-based approaches. The model achieved a Dice Similarity Coefficient (DSC) of 97.45 %, Intersection over Union (IoU) of 95.67 %, and a classification accuracy of 99.35 %, representing a 3.1 % reduction in segmentation error and a 2.4 % increase in classification accuracy over benchmark models. The inference time per image is 1.6541 s on a system with 13 GB RAM, confirming its computational efficiency for clinical deployment. By effectively combining generative modeling, transformer-based segmentation, and deep feature classification, the proposed CVAE-UNETR-ResNet50-VGG16 framework establishes a new performance benchmark for automated brain tumor analysis, offering a quantifiable step forward, ≈ 2.5–3 % improvement, in diagnostic precision, computational efficiency, and medical imaging reliability. Thus, the CVAE-UNETR-ResNet50-VGG16 model offers a measurable 2–3 % performance improvement over current techniques, creating a basis for AI-assisted brain tumor diagnosis and treatment planning. This advancement supports the broader goal of AI-driven healthcare, enhancing early diagnosis and treatment planning for neurological disorders. This hybrid design bridges the gap between data-driven inference and structural MRI priors, enhancing both interpretability and precision in clinical decision-making.