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Improved Brain Tumor Segmentation Using UNet-LSTM Architecture

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Abstract

Brain Tumor is always known for its deadliest behavior and people’s less survival probability against it. It is a complex and life- changing medical condition where the abnormal or dead brain cell grows in and around the brain tissues. In the United States, nearly 87,000 cases are diagnosed each year increasing year by year. Brain tumor is mainly classified into two categories based on their impact on the person: Benign (non-cancerous) and Malignant (cancerous). We only focus on the cancerous tumor as it requires early detection for diagnosis. Brain Tumors are diagnosed based on the four different grades from low grade (1,2) and high grade (3,4). It is one of the hectic tasks for the medical professionals to analyze accurately. We worked on this to make the error- prone segmentation by creating the mask in the tumor region. We used MRI images as our dataset (BraTs2020) to train and segment the tumor successfully. Classes taken for segmentation are Eduma, Background, Enhancing, and Non-enhancing. Previously many methodologies have been used for segmented but we came up with integrating Long Short Term Memory (LSTM) along with U-Net architecture. U-Net is a doubled architecture of the Convolutional Neural Network model with contraction and expansive path. The accuracy, loss, and precision obtained from our work are 0.9916, 0.0240, and 0.9930 respectively.

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Data availability

The dataset used in this research is the BrsTs2020 dataset (Open source) which is called from the Kaggle web source. The link for the dataset is https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation

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Authors and Affiliations

  1. Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology, 400 Feet Outer Ring Rd Avadi, Chennai, Tamil Nadu, 600062, India

    Saran Raj Sowrirajan, Logeshwaran Karumanan Srinivasan, Anisha Devi Kalluri & Ravi Kumar Subburam

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  1. Saran Raj Sowrirajan
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  2. Logeshwaran Karumanan Srinivasan
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  3. Anisha Devi Kalluri
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  4. Ravi Kumar Subburam
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Contributions

Saran Raj S and Logeshwaran K S wrote the manuscript and experimented along with Anisha Devi Kalluri. All authors analyzed the results and reviewed the manuscript.

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Correspondence to Logeshwaran Karumanan Srinivasan.

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This article is part of the topical collection “Emerging Applications of Data Science for Real-World Problems” guest edited by Satyasai Jagannath Nanda, Rajendra Prasad Yadav and Mukesh Saraswat.

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Sowrirajan, S.R., Karumanan Srinivasan, L., Kalluri, A.D. et al. Improved Brain Tumor Segmentation Using UNet-LSTM Architecture. SN COMPUT. SCI. 5, 496 (2024). https://doi.org/10.1007/s42979-024-02799-0

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