Abstract
The most challenging issue in medical image analysis is brain tumor diagnosis. Because brain tumors may have a variety of sizes or textures, the photographs show a lot of variations, making the identification process challenging. Multiple types of cells contribute to the development of brain tumors, and these cells can provide details of tumor. Tumors may appear anywhere, and the position of the tumor reveals data about the cells that are responsible for it, enabling a more accurate diagnosis. Accurate and early brain tumor diagnosis is crucial for the disease’s successful treatment. Early detection can eventually save a life in addition to assisting in the development of better treatments. Recent advances in machine learning algorithms have made it possible to interpret medical images and data without the need for human error or the laborious process of manually diagnosing tumors. When compared to manual, traditional diagnosing procedures, computer-aided processes yield higher outcomes. Finding brain tumors and improving care for people who are affected are the two main objectives of this initiative. When abnormal cell growths appear in the brain, they are considered as tumors, and malignant tumors are referred to as cancer. By using CT or MRI imaging, brain cancer regions are frequently found.
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Patil, S., Jaybhaye, S., Kotgire, S., Raina, S., Bhat, S., Sharma, S. (2023). Brain Tumor Detection and Classification. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. ICTIS 2023. Lecture Notes in Networks and Systems, vol 720. Springer, Singapore. https://doi.org/10.1007/978-981-99-3761-5_35
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DOI: https://doi.org/10.1007/978-981-99-3761-5_35
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