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Brain tumors are considered a rather complex diagnostic problem, requiring timely and correct diagnosis in order to ensure effective treatment. Existing traditional methods of diagnostic dependence are based mainly on the subjective interpretation of medical images in order to make a diagnosis, thus resulting in errors. This is new work on this problem, specifically considering issues dealing with the application of the newest deep learning techniques on MRI images. Front-end development is based on TensorFlow and Flask. The combination of CNN is more specific and customized. A customized version of architecture VGG-16 along with YOLO v4 in real time detects outcome withgreat precision and efficiency. The dataset consists of four totally unique classes of tumors, of glioma, meningioma, no tumor, and pituitary. Updates are constantly made to ensure the model remains robust and adaptive enough according to the clinical data that may change over time. In several datasets, rigorous techniques of image preprocessing are applied to align raw data, which improve the performance of the model. Variations of CNN layer deep systematic experimentation show consistency and reliability in the task of tumor detection and lead to improvements in the accuracy of detection; 12-layer models have shown better performances. Careful assessment of performance measures has been done for various categories of tumor classification in terms of support, precision, recall, and F1 score. This gives a full knowledge of the model's effectiveness and performance. Comparison analysis highlights how changes in CNN architectures generally influence the broad accuracy and efficiency, which explains how feature learning is significant in such complicated tasks like medical images. A proposed framework that provides a reliable and automated method for early tumor diagnosis is quite encouraging for clinical practice. In conclusion, this study demonstrates the potential of integrating CNN-based architectures with real-time object detection frameworks like YOLO to revolutionize neuro-oncology diagnostics, enhancing diagnostic precision, efficiency, and clinical outcomes.

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Enhanced Brain Tumor Detection Using Convolutional Neural Networks and YOLO V4 on MRI Images

  • 会议论文

  • 发表日期：

    2025年

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  • Gaurang Maheshwari,
  • Ananya Vashisht,
  • Ayush Rodwal,
  • Abdulla Kothari,
  • Sudhanshu Gonge

摘要

Brain tumors are considered a rather complex diagnostic problem, requiring timely and correct diagnosis in order to ensure effective treatment. Existing traditional methods of diagnostic dependence are based mainly on the subjective interpretation of medical images in order to make a diagnosis, thus resulting in errors. This is new work on this problem, specifically considering issues dealing with the application of the newest deep learning techniques on MRI images. Front-end development is based on TensorFlow and Flask. The combination of CNN is more specific and customized. A customized version of architecture VGG-16 along with YOLO v4 in real time detects outcome withgreat precision and efficiency. The dataset consists of four totally unique classes of tumors, of glioma, meningioma, no tumor, and pituitary. Updates are constantly made to ensure the model remains robust and adaptive enough according to the clinical data that may change over time. In several datasets, rigorous techniques of image preprocessing are applied to align raw data, which improve the performance of the model. Variations of CNN layer deep systematic experimentation show consistency and reliability in the task of tumor detection and lead to improvements in the accuracy of detection; 12-layer models have shown better performances. Careful assessment of performance measures has been done for various categories of tumor classification in terms of support, precision, recall, and F1 score. This gives a full knowledge of the model's effectiveness and performance. Comparison analysis highlights how changes in CNN architectures generally influence the broad accuracy and efficiency, which explains how feature learning is significant in such complicated tasks like medical images. A proposed framework that provides a reliable and automated method for early tumor diagnosis is quite encouraging for clinical practice. In conclusion, this study demonstrates the potential of integrating CNN-based architectures with real-time object detection frameworks like YOLO to revolutionize neuro-oncology diagnostics, enhancing diagnostic precision, efficiency, and clinical outcomes.