Tumors present in brain remains a significant global health issue, affecting millions annually and presenting various diagnostic and therapeutic challenges. These tumors originate from irregular cell growth in the brain and its surrounding tissues, and their treatment varies based on the location, and severity of the tumor. Timely and precise diagnosis is essential for enhancing patient outcomes, but it remains challenging due to the variability and complexity of brain tumors. The complexities involved in identifying and treating different types of tumors highlight the need for advanced diagnostic tools. Recent developments in brain tumor detection highlight important technological innovations, particularly the use of artificial intelligence (AI), deep learning (DL), and advancements in imaging techniques. In particular, we examine the role of machine learning algorithms, such as Convolutional Neural Networks (CNNs) and You-Only-Look-Once (YOLO), which have enhanced the precision, speed, and accuracy of tumor detection. Furthermore, emerging trends in non-invasive imaging methods and their potential to transform early diagnosis and personalized treatment strategies are explored.


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    Title :

    Classification of Brain Tumor Detection Techniques - A Review


    Contributors:
    Selvi, M (author) / Gokul, K (author) / Dhivin, D (author)


    Publication date :

    2024-11-06


    Size :

    379531 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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