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Health-Care Monitoring for the Brain Tumor Disorder Patients’ by Estimating its Thickness by an Enhanced Capsule Network

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Abstract

The tumor region is the infected portion of the brain that is; present in any area of the human body. The enlargements of the tumor region have to be identified by its thickness, and hence, it improves the patient’s lifetime. Variegated surgical procedures had to carry out in correcting the irregularity in the brain. Likewise, to a certain extent, it has not been worked out. So there is a necessity for pre-treatment measures to analyze the thickness of the irregular region. The current work focussed on this and has been performed in three ways including pre-processing, classification, and segmentation. A modification has been made on the capsule network that has been utilized for the classification to upgrade the performance. An enhanced DWT (Discrete Wavelet Transform) noise replacement technique replaces the Rician noise in the image. Then it enters into CapsNet for training to classify the images, also it generates the output as noise-free images. The enhanced segCaps algorithm has been performed to segment the abnormal region alone. Afterwards its thickness calculation is carried out and verified with the previously available methods to check the proposed methods’ accuracy. It is estimated that, the proposed method is 1.5% better than the previous algorithms.

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Data availability

The dataset used here is the BRATS dataset, which is a publicly available dataset.

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Authors and Affiliations

  1. Dr.NGP Institute of Technology, Coimbatore, India

    R. Remya

  2. Nalla Malla Reddy Engineering College, Hyderabad, India

    K. Parimala Geetha

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  1. R. Remya
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  2. K. Parimala Geetha
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RR: Methodology, literature review; KPG: Content writing, supervision.

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Correspondence to R. Remya.

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Remya, R., Parimala Geetha, K. Health-Care Monitoring for the Brain Tumor Disorder Patients’ by Estimating its Thickness by an Enhanced Capsule Network. Wireless Pers Commun 130, 1743–1757 (2023). https://doi.org/10.1007/s11277-023-10353-z

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