eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-10-22 18 October Spl Edition 68524 article Gaurav Kumar Verma 1 Shailendra Kumar 1 Maninder Singh 2 Harsh Pratap Singh 3 Arvind Mewada 4 Mohd. Aquib Ansari 5 Department of Electronics and Communication Engineering, Integral University, Lucknow, India Department of Symbiosis Centre for Medical Image Analysis, Symbiosis International (Deemed University), Pune, India Department of Computer Science Engineering, Medicapes University, Indore, India Department of Computer Science Engineering, Bennett University, Noida, India SCSE, Galgotias University, Greater Noida, India Brain tumours pose a significant health challenge, necessitating precise classification and segmentation for effective diagnosis and treatment planning. Manual MRI interpretation is time-consuming and prone to subjectivity, highlighting the need for automated, reliable solutions. This paper presents a hybrid model that combines EfficientNetB7 and UNet to provide tumour classification and segmentation inside a single framework, thereby improving diagnostic accuracy and processing efficiency. EfficientNetB7 extracts high-level features from 600*600*3 MRI scans. This facilitates the precise classification into glioma, meningioma, pituitary, and no tumour. The UNet decoder utilises these shared features to generate pixel-wise segmentation maps of the tumour core, edema, and enhancing tumour regions, with the latter being clinically relevant primarily for gliomas. When tested on the BraTS 2020 dataset, the model achieved a classification accuracy of 98.5%, a Dice coefficient of 94.5%, and an Intersection over Union (IoU) of 91.3%, outperforming standalone EfficientNet, UNet, and contemporary hybrid architectures. With a 42 ms inference time per image, the model enables real-time clinical applications. By combining segmentation and classification into a single framework, the EfficientNetB7-UNet model offers a reliable, effective, and practically useful method for automated brain tumour diagnosis. https://biomedpharmajournal.org/vol18octoberspledition/hybrid-approach-for-classification-and-segmentation-of-brain-tumour/ Brain tumour BraTs 2020 EfficientNet Hybrid deep learning IoU MRI U-Net