Brain tumor information retrieval system for brain tumor diagnosis
DOI:
https://doi.org/10.56294/hl2024.179Keywords:
Content-Based Image Retrieval, Information retrieval, brain tumor, Magnetic Resonance ImagingAbstract
Application areas for information retrieval include searching a wide range of information from search engines, identifying defective product parts in industry, extracting valuable knowledge from medical images, quickly identifying criminals in the criminal justice system through facial image and fingerprint analysis, and security biometric applications. For the aforementioned objectives, picture is a necessary component to draw original conclusions. The majority of applications rely heavily on picture retrieval, which is based on two main methods: content-based and text-based methods. One useful method used in image searching applications is Content-Based Image Retrieval (CBIR). Colour, texture, and shape descriptors—low-level traits—are used in CBIR to retrieve images. These descriptions make it simple to determine the image's context. The goal of this work is to identify brain tumour locations in magnetic resonance imaging datasets and to distinguish between normal and defective picture types. Additionally, the suggested approach performs well when it comes to classifying photos for medical applications and identifying specific locations of brain tumours. The importance of this finding prompts the creation of fresh methods for identifying patients' medical problems in real time.
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Copyright (c) 2024 Markhabo Rakhmatova, Shakhnoza Shakhanova, Janna Nazarova, Farida Azizova, Farida Azizova, Dilmurod Astanakulov, Gayratjon Akramov, Nadira Mirametova (Author)
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