Abstract

The identification of tumorous tissues in the brain based on Magnetic Resonance Images (MRI) analysis is a challenging and time consuming task that highly depends on radiologists expertise. As prompt diagnosis of tumors can often be inherent to the patient's survival, it is however crucial to decrease the amount of time spent on the manual analysis of MRI while increasing the accuracy of the detection process. To tackle these issues, many research works have already investigated efficient computer vision systems. They offer new opportunities to assist health care providers in the establishment of fast and more accurate tumor detection, classification and segmentation. However, often based on deep learning methods, the development and tuning of these solutions remains time and energy consuming while inducing a lack of explainability in the decision making system. In this study, we respond to these issues by solving a brain tumor detection task using the Selective Search (SS) algorithm coupled with a simplified Pulse-Coupled Neural Network (PCNN) for visual feature extraction and detection validation. The performed experiments showed promising results in terms of computational cost and detection accuracy. This leads to the development of a light-weight brain tumor detection system.

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Cite this article

@article{cai-IRSH-29,
  author  = {Brad Niepceron and Filippo Grassia and Ahmed Nait Sidi Moh},
  title   = {Brain Tumor Detection Using Selective Search and Pulse-Coupled Neural Network Feature Extraction},
  journal = {Computing and Informatics},
  volume  = {41},
  number  = {1},
  pages   = {253--270},
  year    = {2022},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2022_1_253}
}
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