A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction

Singh, Sanskriti

doi:10.48550/arxiv.2204.01201

Cited by 2 publications

(2 citation statements)

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“…The results obtained for LeNET were a sensitivity of 95%, a specificity of 94% and an accuracy of 94%, and for AlexNET, a sensitivity of 95%, a specificity of 95% and an accuracy of 96%. A few authors have also proposed RCNN to solve the problem of brain tumor segmentation [18,19,20]. Among them, Singh et al [18] trained their model using the Brats 2020 dataset and obtained a sensitivity of 72% and a precision of 79%.…”

Section: Related Workmentioning

confidence: 99%

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NeuroMorphNet: A Multiscale Convolutional Neural Network for High-Precision Brain Tumor Segmentation in 3D Medical Images

Guennich,

Othmani,

Ltifi

2023

Preprint

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The use of high-precision automatic algorithms to segment brain tumors offers the potential for improved disease diagnosis, treatment monitoring, as well as the possibility of large-scale pathological studies. In this study, we present a new 9-layer multiscale architecture dedicated to the semantic segmentation of 3D medical images, with a particular focus on brain tumor images, using convolutional neural networks. Our innovative solution draws inspiration from the Deepmedic architecture while incorporating significant enhancements. The use of variable-sized filters between layers and the early incorporation of residual connections from the very first layer greatly enhance the accuracy of 3D medical image segmentation. Additionally, the reduction in the number of layers in our dual-pathway network optimizes efficiency while maintaining exceptional performance. This combination of innovations, with Deepmedic as a starting point, positions our solution as a major advancement in the field of 3D medical image segmentation, offering an optimal balance between accuracy and efficiency for clinical applications.

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Section: Related Workmentioning

confidence: 99%

“…A few authors have also proposed RCNN to solve the problem of brain tumor segmentation [18,19,20]. Among them, Singh et al [18] trained their model using the Brats 2020 dataset and obtained a sensitivity of 72% and a precision of 79%.…”

Section: Related Workmentioning

confidence: 99%