This project focuses on classifying brain tumor MRI images into 44 different classes using YOLOv8, a state-of-the-art object detection algorithm. The dataset used for training and evaluation is sourced from Kaggle and consists of MRI images with various types of brain tumors.
- Source: Brain Tumor MRI Images
- Total Images: 44 classes with varying image counts (ranging from 31 to 43 images per class)
- Neurocitoma T1C+
- Meduloblastoma T1C+
- Ependimoma T2
- Astrocitoma T1
- _NORMAL T2
- Ganglioglioma T2
- Granuloma T1
- Carcinoma T1C+
- Neurocitoma T2
- Papiloma T1
- Tuberculoma T1C+
- Glioblastoma T2
- Carcinoma T2
- Meningioma T1C+
- Papiloma T2
- Oligodendroglioma T1
- Granuloma T2
- Schwannoma T1
- Astrocitoma T1C+
- Meduloblastoma T1
- Germinoma T2
- Papiloma T1C+
- Glioblastoma T1C+
- Ependimoma T1C+
- Oligodendroglioma T2
- Carcinoma T1
- Ependimoma T1
- Neurocitoma T1
- Schwannoma T2
- Astrocitoma T2
- Ganglioglioma T1C+
- Glioblastoma T1
- Schwannoma T1C+
- Ganglioglioma T1
- Germinoma T1C+
- Meduloblastoma T2
- Tuberculoma T1
- Germinoma T1
- Oligodendroglioma T1C+
- _NORMAL T1
- Tuberculoma T2
- Granuloma T1C+
- Meningioma T2
- Meningioma T1
- Accuracy: 97%
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- Training: Use the provided dataset to train the YOLOv8 model.
- Inference: Once trained, the model can be used to classify brain tumor MRI images. Provide an image to the model, and it will predict the class of the brain tumor present.
Got it! Here's the updated section:
import zipfile
import os
import shutil
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
from ultralytics import YOLO
Created by Bharat_Sharma
for any suggestion, Queries feel free to contact bharat05122002@gmail.com