# Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
## 4 Discussions  
The multi-level threshold classifier's test performance was assessed using the statistical metrics shown in Figure 6, such as accuracy, sensitivity, precision, and Dice Index. Additionally, higher accuracy and sensitivity ratings suggest better performance. Images that have been segmented (10 is represented out of 40 images). The accuracy of the entire set of photos is 99.5 percent on average, with a standard deviation of 0.07 percent. It demonstrates that the segmentation is extremely accurate. With a standard deviation of roughly 3.7 percent, the average precision obtained is around 97.5 percent. With a standard variation of roughly 3.6 percent, the average Dice result is around 87 percent. With a standard deviation of roughly 5%, the average sensitivity is around 78 percent. The global assessment of the detection of the tumor combining all four performance evaluation metrics on average is about 90.5%. Figure 6 illustrates the promising performance of our segmentation technique. For a radiologist or clinical practitioner, even a small increase in the sensitivity parameter is crucial for surgical planning. To compare our results to the state-of-the-art results. The selection of those methods was driven by the fact that they took part in the international \"BraTS-2017-2021\" challenge and utilized datasets from that competition. These datasets were manually segmented by subject-matter experts, and the best segmentation results were then made public. Other methods were excluded because they use different datasets and/or do not calculate the Dice Index. Different methods, including neural network and deep learning methods (Single path MLDeepMedic, U-Net, Rescue Net, and Cascaded Anistropic CNN) and a classical segmentation method were used in this comparison. The comparison is presented in Table 1 below. The average performance of the Dice index and average time of execution are shown. The dice index was chosen because of its widespread use in the literature and the availability of Dice results for all compared methods. Although all of the compared methods use the same datasets, the number of photos in our experimental data differs from the number of images in the other approaches. The time for CNN and DLA was estimated using about 370 images for training on HP, core I7 computer, and using 40 iterations. The average dice index of 40 photographs is compared with others which are statistically representative of the entire collection of data assuming a Gaussian distribution of dice index among datasets.  
Figure 5: Shows a series of slices of 3D extracted tumor compared with the Ground Truth (GT), where the green is a normal tissue selected as a tumor and the purple is a tumorous tissue selected as normal.  
Table 1 shows that our suggested method outperforms the unsupervised methods \"K mean and FCM.\" It is comparable to the \"Cascaded Anistropic CNN,\" and is somewhat better than \"Single path ML DeepMedic\" and \"UNet.\" It shows less performance than the Rescue Net approach, which is a supervised deep learning method that requires several hours of time and a huge number of images for the learning process as well as a lot of computer capacity. Our approach is also capable of accurately segmenting entire tumor regions, as seen in Figures 3 and 4.