Welcome to the Brain MRI Images for Brain Tumor Detection project! This project involves the development of a deep learning model to detect brain tumors in MRI images. The goal is to classify brain MRI scans into two classes: "yes" indicating the presence of a tumor and "no" indicating a healthy brain.
The project utilizes a dataset containing a collection of brain MRI images. The dataset consists of 255 images, with two classes: "yes" (brain with a tumor) and "no" (healthy brain). This dataset serves as the basis for training and evaluating the tumor detection model.
In this project, We Did:
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Data Exploration: Examine the dataset to understand its characteristics, such as image size, distribution of classes, and data quality.
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Data Preprocessing: Prepare the MRI images for model training by standardizing image sizes, normalizing pixel values, and addressing any data quality issues.
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Model Development: Develop a deep learning model for brain tumor detection using convolutional neural networks (CNNs) or similar architectures.
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Model Training: Train the model on the preprocessed dataset, optimizing hyperparameters and monitoring training metrics.
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Model Evaluation: Evaluate the trained model's performance on a separate test dataset, using metrics such as accuracy, precision, recall, and F1-score.
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Deployment: Deploy the trained model to make it accessible for real-world use. We deployed it using Streamlit.
The main objective of this project is to create an accurate and reliable model for detecting brain tumors in MRI images. The successful completion of this task can have significant implications in medical diagnosis and healthcare by aiding in the early detection of brain tumors, potentially saving lives and improving patient outcomes.
