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Segmentation and identification of brain tumour in MRI images using PG-OneShot learning CNN model

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Abstract

Brain tumour segmentation plays a critical role in the diagnosis, treatment planning, and monitoring of brain tumour patients. However, accurate and efficient segmentation remains challenging due to the complex and heterogeneous structure of brain tumour regions. The current CNN models have shown good performance in brain tumour segmentation and identification, but several research challenges, like limited generalizability, Adaptive Model Complexity, etc., still need to be addressed. In this research, we propose a novel approach that combines the progressively growing and One-Shot learning approaches with a semantic segmentation network to enhance the accuracy and generalization of brain tumour segmentation in MRI images. Our method joins the strengths of progressively growing and One-Shot learning techniques with a semantic segmentation network, enabling improved generalization, effective feature selection, and continuous integration of contextual information at the pixel level. Experimental results on benchmark Br35H MRI image datasets demonstrate the dominance of our approach over existing methods in terms of segmentation accuracy and adaptability to diverse brain tumour instances. A total of 3000 images (1500 tumorous and 1500 non-tumorous images) were used during the training and testing of the model. The evaluation metrics reveal the high performance of our proposed model for brain tumour segmentation. Achieving high Dice Similarity Coefficients (0.9849), Intersection over Union (0.9319), accuracy (0.9520), precision (0.9235), and recall (0.9572) across average training, validation, and test sets. These results demonstrate the model's efficiency in accurately segmenting both tumorous and non-tumorous regions in MRI images.

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Data availability

The data used in this paper can be accessed from the KAGGLE online database.

https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection

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Acknowledgements

We are very thankful to Wuhan University for her generous support in conducting this research.

Funding

This work was sponsored by the National Natural Science Foundation of China General Program with grant number (No. 6 2272352).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China

    Azmat Ali & Yulin Wang

  2. School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, Hubei, China

    Xiaochuan Shi

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  1. Azmat Ali
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Azmat Ali proposed the ideas, collected data, and wrote the manuscript. Yulin Wang and Xiaochuan Shi provided the idea and discussed the outline of the manuscript.

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Correspondence to Yulin Wang.

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Ali, A., Wang, Y. & Shi, X. Segmentation and identification of brain tumour in MRI images using PG-OneShot learning CNN model. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18596-z

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