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Brain Tumor Segmentation through Level Based Learning Model

K. Dinesh Babu1,*, C. Senthil Singh2

1 Department of Electronics and Communication Engineering, Adhi College of Engineering and Technology, 631 605, India
2 Department of Electronics and Communication Engineering, Infant Jesus College of Engineering, Vallanadu, Thoothukudi, 628 851, India

* Corresponding Author: K. Dinesh Babu. Email: email

Computer Systems Science and Engineering 2023, 44(1), 709-720. https://doi.org/10.32604/csse.2023.024295

Abstract

Brain tumors are potentially fatal presence of cancer cells over a human brain, and they need to be segmented for accurate and reliable planning of diagnosis. Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived. Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities. The scanner used for sensing the location of tumors cells will be subjected to additional protocols and measures for accuracy, in turn, increasing the time and affecting the performance of the entire model. In this view, Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising results. The previous strategies and models failed to adhere to diversity of sizes and shapes, proving to be a well-established solution for detecting tumors of bigger size. Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network (CNN). This proposal intends to furnish a detailed model for sensing early stages of cancer and hence perform segmentation irrespective of the current size and shape of tumors. The size of networks and layers will lead to a significant weightage when multiple kernel sizes are involved, especially in multi-resolution environments. On the other hand, the proposed model is designed with a novel approach including a dilated convolution and level-based learning strategy. When the convolution process is dilated, the process of feature extraction deals with multiscale objective and level-based learning eliminates the shortcoming of previous models, thereby enhancing the quality of smaller tumors cells and shapes. The level-based learning approach also encapsulates the feature reconstruction processes which highlights the sensing of small-scale tumors growth. Inclusively, segmenting the images is performed with better accuracy and hence detection becomes better when compared to that of hierarchical approaches.

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Cite This Article

APA Style
Babu, K.D., Singh, C.S. (2023). Brain tumor segmentation through level based learning model. Computer Systems Science and Engineering, 44(1), 709-720. https://doi.org/10.32604/csse.2023.024295
Vancouver Style
Babu KD, Singh CS. Brain tumor segmentation through level based learning model. Comput Syst Sci Eng. 2023;44(1):709-720 https://doi.org/10.32604/csse.2023.024295
IEEE Style
K.D. Babu and C.S. Singh, "Brain Tumor Segmentation through Level Based Learning Model," Comput. Syst. Sci. Eng., vol. 44, no. 1, pp. 709-720. 2023. https://doi.org/10.32604/csse.2023.024295



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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