Skip to main content

Research Repository

Advanced Search

DeepCAI-V3: Improved Brain Tumor Classification from Noisy Brain MR Images using Convolutional Autoencoder and Inception-V3 Architecture

Babaferi, Evuarhere Vivian; Fagbola, Temitayo Matthew; Thakur, Colin Surendra

Authors

Evuarhere Vivian Babaferi

Colin Surendra Thakur



Abstract

Brain tumors are abnormal cell growths within the brain tissues, necessitating their early detection towards effective treatment. To achieve this, high-quality brain images via medical imaging techniques, such as Magnetic Resonance Imaging (MRI), are essential for accurate brain health diagnosis. However, such MR images are often noise-prone, due to potential interference from various sources, including scanner artifacts, patient motion and intensity variations during capture, processing and storage steps. This noise can significantly hinder accurate classification of brain tumors. Consequently, most state-of-The-Art methods for brain tumor detection struggle to perform well with noisy MR images as their error margins widen. Therefore, accurate brain tumor classification of noisy MR images remains an open problem. In this paper, DeepCAI-V3, a two-stage deep learning architecture, is proposed for improved brain tumor classification from Noisy MR images (NMRI). First, a convolutional Autoencoder is employed to pre-process and denoise NMRI. Subsequently, Inception-V3architecture is used for feature extraction and classification. DeepCAI-V3model was validated on the publicly available Brain MRI and Brain Tumor MRI datasets which are augmented with zero-mean Gaussian noise. Evaluation results indicate that DeepCAI-V3outperforms state-of-The-Art methods having achieved accuracy of 97% and 99.5% on Brain MRI and Brain Tumor MRI datasets respectively, demonstrating its robustness against noise artifacts in brain MR images.

Citation

Babaferi, E. V., Fagbola, T. M., & Thakur, C. S. (2024, August). DeepCAI-V3: Improved Brain Tumor Classification from Noisy Brain MR Images using Convolutional Autoencoder and Inception-V3 Architecture. Presented at 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, Mauritius

Presentation Conference Type Conference Paper (published)
Conference Name 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems
Start Date Aug 1, 2024
End Date Aug 2, 2024
Acceptance Date May 10, 2024
Online Publication Date Aug 29, 2024
Publication Date Aug 29, 2024
Deposit Date Jun 21, 2024
Publicly Available Date Aug 30, 2026
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Book Title 2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)
ISBN 979-8-3503-8790-2
DOI https://doi.org/10.1109/icABCD62167.2024.10645225
Keywords MRI Denoising; Convolutional Autoencoder; InceptionV3; Brain Tumor Classification; Transfer Learning
Public URL https://hull-repository.worktribe.com/output/4716130
Publisher URL https://ieeexplore.ieee.org/document/10645225