Brain Tumor classification and Segmentation using Deep Learning

• URL: http://arxiv.org/abs/2304.07901v1
• Date: Sun, 16 Apr 2023 21:42:21 GMT
• Title: Brain Tumor classification and Segmentation using Deep Learning
• Authors: Belal Badawy, Romario Sameh Samir, Youssef Tarek, Mohammed Ahmed, Rana Ibrahim, Manar Ahmed, Mohamed Hassan
• Abstract summary: We present a machine learning-based system designed to assist healthcare professionals in the classification and diagnosis of brain tumors using MRI images. Our system provides a secure login, where doctors can upload or take a photo of MRI and our app can classify the model and segment the tumor. Our system can classify in less than 1 second and allow doctors to chat with a community of brain tumor doctors.
• Score: 3.1248717814228923
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Brain tumors are a complex and potentially life-threatening medical condition that requires accurate diagnosis and timely treatment. In this paper, we present a machine learning-based system designed to assist healthcare professionals in the classification and diagnosis of brain tumors using MRI images. Our system provides a secure login, where doctors can upload or take a photo of MRI and our app can classify the model and segment the tumor, providing the doctor with a folder of each patient's history, name, and results. Our system can also add results or MRI to this folder, draw on the MRI to send it to another doctor, and save important results in a saved page in the app. Furthermore, our system can classify in less than 1 second and allow doctors to chat with a community of brain tumor doctors. To achieve these objectives, our system uses a state-of-the-art machine learning algorithm that has been trained on a large dataset of MRI images. The algorithm can accurately classify different types of brain tumors and provide doctors with detailed information on the size, location, and severity of the tumor. Additionally, our system has several features to ensure its security and privacy, including secure login and data encryption. We evaluated our system using a dataset of real-world MRI images and compared its performance to other existing systems. Our results demonstrate that our system is highly accurate, efficient, and easy to use. We believe that our system has the potential to revolutionize the field of brain tumor diagnosis and treatment and provide healthcare professionals with a powerful tool for improving patient outcomes.

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