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README.md

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 # Glioblasted (Prototype)
 
 ## Summary
-Glioblasted is a software utilizing image classification to detect glioblastoma, a high-grade, aggressive form of brain and/or spine cancer, given images of H&E stained slides. Glioblasted has an accuracy of 98% and is trained on data from The Cancer Imaging Archive (TCIA)'s University of Pennsylvania Glioblastoma Multiforme (UPENN-GBM) dataset (Link: [UPENN-GBM](https://pathdb.cancerimagingarchive.net/eaglescope/dist/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22UPENN-GBM%22%7D%5D). This repository hosts a prototype (v0.1.0α) for instruction on elementary image classification.
+Glioblasted is a software utilizing image classification to detect glioblastoma, a high-grade, aggressive form of brain and/or spine cancer, given images of H&E stained slides. Glioblasted has an accuracy of 98% and is trained on data from The Cancer Imaging Archive (TCIA)'s University of Pennsylvania Glioblastoma Multiforme (UPENN-GBM) dataset (Link: [UPENN-GBM](https://pathdb.cancerimagingarchive.net/eaglescope/dist/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22UPENN-GBM%22%7D%5D)). This repository hosts a prototype (v0.1.0α) for instruction on elementary image classification.
 
 ## Abstract
 This study examined how glioblastoma, a high-grade cancer originating in brain or spine cells, can be detected using machine learning with a 98% accuracy or above to fix the lack of available tools to detect glioblastoma via histological slides. Using data collected from The Cancer Imaging Archive, a Python-based Scikit-learn model was created that could detect between benign and malignant tumors of glioblastoma. However, the accuracy of the model was low. To combat this, more accurate layers were added and various numbers in the model (i.e. epochs) were modified. The majority of the procedure involved the improvement of the input image through the image processing Python module OpenCV's various functions (i.e. thresholds, contours, inverses) in order to achieve the desired accuracy. In the end, an accuracy level of 98% was achieved. The likely reasons for this are the various modifications that were made in newer prototypes. It can be concluded that this tool is a step towards improving the field of glioblastoma-related AI-assisted pathology. Theoretical and practical implications of the results were discussed.