Automated Brain Tumor Detection Using Convolutional Neural Networks in Computer-Aided Diagnosis Systems

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@article{1708532,
      author = {Rakesh Jindal, Amisha Naik, K L Ganatre, Rajat Gupta},
      title = {Automated Brain Tumor Detection Using Convolutional Neural Networks in Computer-Aided Diagnosis Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {6},
      pages = {422-433},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708532.pdf},
      abstract = {This research focuses on leveraging Convolutional Neural Networks (CNNs) to build Computer-Aided Diagnosis (CAD) systems for detecting brain tumors. Due to the ongoing global impact of brain tumors on health, early and accurate diagnosis is critical to enhancing patient survival rates. Conventional diagnosis depends heavily on radiologists' expertise, which can be subjective and slow. CNNs, as a powerful deep learning tool, excel in extracting complex features from medical images, particularly Magnetic Resonance Imaging (MRI), with high accuracy and speed. The study evaluates the performance of various CNN models, including both custom and pre-trained networks, for identifying and categorizing different brain tumor types. Key methodological aspects include dataset selection (e.g., BraTS), preprocessing steps like normalization and augmentation, and a rigorous training-validation-testing framework. Metrics such as accuracy, precision, recall, F1-score, and AUC-ROC assess performance. Visualization techniques like Grad-CAM are employed to interpret model decisions and highlight tumor locations, enhancing transparency. Findings demonstrate that CNN-based CAD tools can substantially improve diagnostic efficiency and precision, making them valuable in clinical practice. Challenges such as limited data, variability across imaging devices, and model explainability are also discussed. The paper concludes by proposing future directions involving multi-modal data fusion and Explainable AI (XAI) to increase clinical trust and aid decision-making. Overall, the study emphasizes the promising role of CNNs in automated, intelligent brain tumor diagnosis.},
      keywords = {Brain Tumor Detection, CNN, Computer-Aided Diagnosis, MRI, Deep Learning, Medical Imaging, Automated Diagnosis, Radiology, Feature Extraction, Transfer Learning, Diagnostic Performance, Neuroimaging, Clinical Support, AI in Healthcare.},
      month = {December},
  }