Mask RCNN on brain tumor.pptx
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The document describes using Mask R-CNN to detect brain tumors from MRI scans. It discusses how Mask R-CNN works, including its convolutional and pooling layers. The methodology section explains that the model uses a region proposal network to identify regions of interest, an object detection branch to classify ROIs as tumor or non-tumor, and a segmentation mask to identify tumor boundaries. The results found a mean precision of 0.81, recall of 0.74, and F1 score of 0.83, showing Mask R-CNN can accurately detect and segment brain tumors from MRI images.
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Mask RCNN on brain tumor.pptx
- 1. Brain Tumor Detection Using Mask R-CNN Presented By: 20051676 – Danish Khan
- 2. Introduction ● Brain tumors are the abnormal development of tissues in the brain, and they can be efficiently treated if detected early. The formation of abnormal brain tissue is the primary cause of a brain tumor. ● Deep Learning algorithms are used in numerous research publications to detect brain tumors. When these algorithms are applied to MRI scans, the prediction of brain tumors is very quick, and the increased accuracy aids in the treatment of patients. ● A self-defined Convolution Neural Network (CNN) is used in the proposed project to identify the existence of brain tumors, and their performance is evaluated.
- 3. CONVOLUTIONAL NEURAL NETWORK(CNN) A Convolutional Neural Network (CNN) is a sort of artificial neural network that is tuned to analyze pixel input and is used in image recognition and processing. The Convolutional Neural Network Architecture is divided into three layers: ● Convolutionallayer:This layer uses filters and kernels to abstract the input picture as a feature map. ● Poolinglayer:This layer aids in the downsampling of feature maps by summarizing the presence of features in feature map patches. ● Fullyconnectedlayer:Layers that are fully linked connect every neuron in one layer to every neuron in another layer.
- 4. Mask R-CNN Mask R-CNN is a popular deep learning instance segmentation technique that performs pixel-level segmentation on detected objects. The Mask R-CNN algorithm can accommodate multiple classes and overlapping objects.
- 6. Methodology This Section specifics of our proposedapproaches. 1)Data Pre-processing 1)Region Of Interest Align 1)Region Proposal Network 1)Segmentation Mask 1)Object Detection Branch
- 7. Data Pre-Processing ● The dataset images are already pre-processed and are in grayscale format. Each test ,train and validate folders contains a JSON file. ● JSON is a file containing data in the form of x-y coordinates of polygon which represents the custom masked tumors. ● This file is used in training the Mask R-CNN model to locate the exact location of the tumor. ● These polygons of tumor in JSON file help to validate and test the model by comparing our results to actual ground truth of tumor.
- 8. Proposed Methodology 1)Regionproposalnetwork-Alight weight neural network called RPN scans all FPN top-bottom pathway( hereinafter referred to feature map)and proposes regions which may contain objects. 2)RegionOf InterestAlign:Thistakes feature map and the Proposed ROI (found in RPN) as input,and classifies ROIs to a specific class such as tumor/non tumor.
- 9. Proposed Methodology Cont. 3) Object Detection Branch:This stage processes the ROIs proposed by RPN. For each ROI, two different outputs are generated: Class and Bounding Box. 4) Segmentation mask:The segmentation network takes positive ROI identified by the ROI classifier as input and returns a segmentation mask.
- 11. Modification There were three main modification done by us: 1. We found dataset from an existing github repository and changed the folder structure according to need. 2. Originally, the Mask R-CNN model was trained on the COCO dataset. We have added a custom dataset of brain tumours to the existing model. 3. To increase accuracy, we have further changed the existing neural network layers in the RCNN model.
- 12. Results Analysis Mean Precision 0.8133959593449857 Mean Recall 0.7360805437299206 Mean F1 Score 0.8275687339969712
- 13. Conclusion This experiment shows that the best strategy for predicting brain cancer is to first utilise MRI scanned pictures to detect the tumours inside them using Mask R-CNN. The Mask R-CNN model, many machine learning methods, and other crucial pre-processing stages are used to implement the suggested method. We have a 67% prediction accuracy for the dataset. The technique has shown to be effective in identifying the tumor size and location from a brain MRI image.