SKRIPSI Computer Vision Project
Updated 2 years ago
Here are a few use cases for this project:
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Medical Diagnostics: Doctors and radiologists can use the SKRIPSI model to assist them in diagnosing gliomas and other brain tumors such as meningioma and pituitary tumors from MRI scans, leading to faster and more accurate diagnosis.
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Tumor Progression Monitoring: The SKRIPSI model can help medical professionals track the growth or regression of gliomas, meningiomas, and pituitary tumors over time by comparing and analyzing sequential MRI scans, allowing for better assessment of treatment effectiveness.
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Clinical Research: Researchers studying gliomas and other brain tumors can utilize the SKRIPSI model to efficiently identify and classify tumor types in large datasets, enabling them to analyze patterns, associations, and trends related to these tumors.
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Artificial Intelligence in Healthcare: Developers working on AI-driven healthcare applications could integrate the SKRIPSI model into their platforms, enabling professionals to quickly and accurately identify gliomas and other brain tumors using computer vision technology.
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Medical Education: The SKRIPSI model can be used as a training tool for medical students and residents, helping them learn to identify and differentiate between gliomas, meningiomas, and pituitary tumors from MRI scans, and improving their diagnostic skills.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
skripsi-vud9c_dataset,
title = { SKRIPSI Dataset },
type = { Open Source Dataset },
author = { amalia.fitri.inaba-2018@fst.unair.ac.id },
howpublished = { \url{ https://universe.roboflow.com/amalia-fitri-inaba-2018-fst-unair-ac-id/skripsi-vud9c } },
url = { https://universe.roboflow.com/amalia-fitri-inaba-2018-fst-unair-ac-id/skripsi-vud9c },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { oct },
note = { visited on 2024-11-08 },
}