EDLNet: ensemble deep learning network model for automatic brain tumor classification and segmentation

Abstract

The brain’s abnormal and uncontrollable cell partitioning is a severe cancer disease. The tissues around the brain or the skull induce this tumor to develop spontaneously. For the treatment of a brain tumor, surgical techniques are typically preferred. Deep learning models in the biomedical field have recently attracted a lot of attention for detecting and treating diseases. This article proposes a new Ensemble Deep Learning Network (EDLNet) model. This research uses the Modified Faster RCNN approach to classify brain MRI scan images into cancerous and non-cancerous. A deep recurrent convolutional neural network (DRCNN)-based diagnostic method for early-stage brain tumor segmentation is presented. The evaluation outcomes show that the proposed tumor classification and segmentation model’s performance accurately segments tissues from MRI images. For the analysis of the proposed model, two different publicly available datasets (D1&D2) are used. For D1 and D2 datasets, a total of 99.76% and 99.87% accuracies are achieved by the proposed model. The performance results of the proposed model are more effective than the state-of-the-art network models as per the experimental results.

Communicated by Ramaswamy H. Sarma

Keywords:

• Tumor classification
• early stages
• image processing
• deep learning
• biomedical field

Acknowledgements

We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

Authors’ contributions

The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

Conflict of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Data availability statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethics approval

This material is the authors’ own original work, which has not been previously published elsewhere. The article reflects the authors’ own research and analysis in a truthful and complete manner.

Additional information

Funding

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