An attention 3DUNET and visual geometry group-19 based deep neural network for brain tumor segmentation and classification from MRI

Abstract

There has been an abrupt increase in brain tumor (BT) related medical cases during the past ten years. The tenth most typical type of tumor affecting millions of people is the BT. The cure rate can, however, rise if it is found early. When evaluating BT diagnosis and treatment options, MRI is a crucial tool. However, segmenting the tumors from magnetic resonance (MR) images is complex. The advancement of deep learning (DL) has led to the development of numerous automatic segmentation and classification approaches. However, most need improvement since they are limited to 2D images. So, this article proposes a novel and optimal DL system for segmenting and classifying the BTs from 3D brain MR images. Preprocessing, segmentation, feature extraction, feature selection, and tumor classification are the main phases of the proposed work. Preprocessing, such as noise removal, is performed on the collected brain MR images using bilateral filtering. The tumor segmentation uses spatial and channel attention-based three-dimensional u-shaped network (SC3DUNet) to segment the tumor lesions from the preprocessed data. After that, the feature extraction is done based on dilated convolution-based visual geometry group-19 (DCVGG-19), making the classification task more manageable. The optimal features are selected from the extracted feature sets using diagonal linear uniform and tangent flight included butterfly optimization algorithm. Finally, the proposed system applies an optimal hyperparameters-based deep neural network to classify the tumor classes. The experiments conducted on the BraTS2020 dataset show that the suggested method can segment tumors and categorize them more accurately than the existing state-of-the-art mechanisms.

Communicated by Ramaswamy H. Sarma

Keywords:

• Brain tumor segmentation
• brain tumor classification
• deep learning
• three-dimensional U-shaped network (3DUNet)
• BraTS2020
• three-dimensional magnetic resonance imaging

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Availability of data and materials

The experimentations are accomplished over the BraTS2020 dataset and is collected from Kaggle through https://www.kaggle.com/datasets/awsaf49/brats2020-training-data.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.