Efficient radio surgery segmentation of different brain tumors using deep learning
Abstract
Author(s): Vijay Upadhye, Tarang Bhatnagar, Avadhesh Kumar, Iype Cherian, Sandeep Kumar C, Shikhar Gupta
Background: A feature of medical image processing poses a significant barrier in brain tumor segmentation. Patients' chances of receiving effective therapy and of surviving their brain tumors are greatly enhanced by early diagnosis. Separating brain tumors by hand from the enormous amount of MRI images produced in clinical practice to make a cancer diagnosis is a difficult and time-consuming task. Deep Learning (DL) models are being used to categorize medical pictures semantically at a quick pace.
Methods: In this work, used clinical stereotactic radio surgical dataset as a baseline to compare cutting-edge DL segmentation methods. Herein, proposed a novel DL based Hyper Automated Densely Fused Convolutional Neural Network (HADF-CNN) approach for brain tumor segmentation. To analyze the efficiency of the proposed method, initially, data were collected and preprocessed using Median Filter (MF) to remove unwanted noise. Next, the brain data is segmented using the suggested methodology. The categorization of brain tumors is done using the Deep Support Vector Machine (DSVM).
Results: To compared the proposed model performance with certain conventional segmentation methods. According to experimental data, the suggested approach performs better than traditional approaches.
Conclusion: Based on the data in this study, might draw the conclusion that DL has potential to help with brain lesion segmentation.
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Editors List
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Prof. Elhadi Miskeen
Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia
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Ahmed Hussien Alshewered
University of Basrah College of Medicine, Iraq
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Sudhakar Tummala
Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh
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Alphonse Laya
Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis
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Fava Maria Giovanna
Onkologia i Radioterapia peer review process verified at publons
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