Work

Deep Learning-Based Brain Tumor Detection and Classification from MRI Images with Web and Mobile Interface

Year: 2026

Type: article

Abstract: – The early detection of brain tumours is crucial in ensuring effective treatment. A deep learning-based brain tumour detection and classification automation system is presented using the magnetic resonance imaging (MRI) scans. This proposed method involves the use of CNN-based Xception architecture model for brain tumour image classification. These MRI image scans undergo normalization and augmentation for enhanced performance. A Flask web application and mobile pplication have been developed for predicting and handling the data entries and searches. The use of a mobile application makes it easier for users to interact with the system via mobile devices. Experiments reveal that the proposed method is efficient in detecting brain tumours with an accuracy results.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20472516

Authors: Dr. Singam Aruna, Kornana Triveni, Savara Puja, Maddi Poojitha, Kuppili Jayasri, Ommi Lahari

Institution: Andhra University

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7162941024

FWCI:

Cites:

Cited by:

Related to: 10