Affiliations: Laboratory of Condensed Matter and Interdisciplinary Sciences “CNRST Labeled Research Unit”, URL-CNRST, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco
Correspondence:
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Corresponding author: Kamal Halloum, Condensed Matter and Interdisciplinary Sciences Laboratory, Faculty of Sciences Rabat, Rabat, Morocco. E-mail: halloum.kamel@gmail.com.
Abstract: The segmentation of cancerous tumours, particularly brain tumours, is of paramount importance in medicine due to its crucial role in accurately determining the extent of tumour lesions. However, conventional segmentation approaches have proven less effective in accurately delineating the exact extent of brain tumours, in addition to representing a time-consuming task, making it a laborious process for clinicians. In this study, we proposed an automatic segmentation method based on convolutional neural networks (CNNs), by developing a new model using the Resnet50 architecture for detection and the DrvU-Net architecture, derived from the U-Net model, with adjustments adapted to the characteristics of the medical imaging data for the segmentation of a publicly available brain image dataset called TCGA-LGG and TCIA. Following an in-depth comparison with other recent studies, our model has demonstrated its effectiveness in the detection and segmentation of brain tumours, with accuracy rates for accuracy and the Dice Similarity Coefficient (DSC), the Similarity Index (IoU) and the Tversky Coefficient reaching 96%, 94%, 89% and 91.5% respectively.
Keywords: Brain tumor, classification, segmentation, CLAHE, data augmentation, resnet50, U-Net
