Brain tumor segmentation by optimizing deep learning U-Net model.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine
Published Date:

Abstract

BackgroundMagnetic Resonance Imaging (MRI) is a cornerstone in diagnosing brain tumors. However, the complex nature of these tumors makes accurate segmentation in MRI images a demanding task.ObjectiveAccurate brain tumor segmentation remains a critical challenge in medical image analysis, with early detection crucial for improving patient outcomes.MethodsTo develop and evaluate a novel UNet-based architecture for improved brain tumor segmentation in MRI images. This paper presents a novel UNet-based architecture for improved brain tumor segmentation. The UNet model architecture incorporates Leaky ReLU activation, batch normalization, and regularization to enhance training and performance. The model consists of varying numbers of layers and kernel sizes to capture different levels of detail. To address the issue of class imbalance in medical image segmentation, we employ focused loss and generalized Dice (GDL) loss functions.ResultsThe proposed model was evaluated on the BraTS'2020 dataset, achieving an accuracy of 99.64% and Dice coefficients of 0.8984, 0.8431, and 0.8824 for necrotic core, edema, and enhancing tumor regions, respectively.ConclusionThese findings demonstrate the efficacy of our approach in accurately predicting tumors, which has the potential to enhance diagnostic systems and improve patient outcomes.

Authors

  • Abdullah A Asiri
    Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Saudi Arabia.
  • Lal Hussain
    Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan.
  • Muhammad Irfan
    Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, GC University Faisalabad, Pakistan.
  • Khlood M Mehdar
    Department of Anatomy, Medicine College, Najran University, Najran 61441, Saudi Arabia.
  • Muhammad Awais
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Magbool Alelyani
    Department of Radiological Sciences, College of Applied Medical Science, King Khalid University, Abha, Kingdom of Saudi Arabia.
  • Mohammed Alshuhri
    Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Kingdom of Saudi Arabia.
  • Ahmad Joman Alghamdi
    Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif, Kingdom of Saudi Arabia.
  • Sultan Alamri
    Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif, Kingdom of Saudi Arabia.
  • Muhammad Amin Nadeem
    Department of Learning Sciences and Digital Technologies, University of Foggia, Foggia, Italy.

Keywords

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