Computer-Aided Detection of Brain Tumour in Humans

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@article{1708842,
      author = {Akshay Bhatia, Kamal Jyoti, Ayushi Upreti, Aniket Tripathi, Simran Sharma},
      title = {Computer-Aided Detection of Brain Tumour in Humans},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {507-516},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708842.pdf},
      abstract = {One of the main challenges in neuro-oncology is brain tumours and it is important to notice them early to give patients a higher chance of successful recovery. More recently, Computer-Aided Detection (CAD) has been greatly impacting medical imaging, especially in discovering and categorizing brain tumours. Using machine learning and deep learning, as well as advanced algorithms, CAD systems improve the accuracy and rapidity of tumour detection in both MRI and CT scans. This article discusses how various CAD approaches are created and used for the identification of brain tumours. They manage important parts of medical image analysis, including image preparation, division into parts, retrieving key features and labelling them based on how they look. Automation enables radiologists to minimize misdiagnoses, reduce the effect of observers? differences and support good decisions in challenging cases. The latest studies have proven that CNNs and hybrid models are better than traditional rule-based systems at identifying and distinguishing benign from malignant brain tumours. Furthermore, including different imaging techniques in CAD applications makes it easier to diagnose patients accurately. There are still issues with CAD systems, including different types of data, not much-labeled training data and having to be validated by clinicians. The article gives an in-depth explanation of CAD methods, looks at how they diagnose conditions and explores new areas such as explainable AI and federated learning. By reading this paper, researchers and clinicians can gain detailed knowledge of how CAD systems play a vital role in brain tumour diagnosis and bring new, personalized and data-driven options to healthcare.},
      keywords = {Brain Tumour Detection; Computer-Aided Detection (CAD); Medical Imaging; Deep Learning; MRI Analysis},
      month = {December},
  }