Proposed System Architecture
The project aims to be able to identify the presence of tumors in brain and breast scans. The first step of the project is to classify whether a given scan is a brain or breast scan. After determing the type of the scan, we classify whether a tumor is presnet or not, If a tumor is present it is segmented and displayed as a binary image.
- Apply image classification to classify each image as a brain scan or a breast scan and then determine if the scan is normal or if it has a tumor.
- Apply image segmentation to determine the exact location of the infected area.
- Determine the width and height of the tumor in the scan if it exists.
The dataset for this project can be found Here Data Set Samples
Brain Scan with Correpsonding Mask
Breast Scan with Correpsonding Mask
Below is breif description of the results of models used
- Model: VGG16-Based Classifier
- Test Loss: 0.0000
- Test Accuracy: 1.0000
- Test Precision: 1.0000
- Test Recall: 1.0000
- Test F1 Score: 1.0000
Brain / Breast Classifier Results
- Model: VGG16-Based Classifier
- Test Loss: 0.189
- Test Accuracy: 0.935
- Test Precision: 0.93
- Test Recall: 0.939
- Test F1 Score: 0.934
Brain Tumor Classifier Results
- Model: VGG16-Based Classifier
- Test Loss: 0.7698
- Test Accuracy: 0.6667
- Test Precision: 0.7197
- Test Recall: 0.6667
- Test F1 Score: 0.6694
Breast Tumor Classifier Results
- Test Loss = 0.112
- Test Dice Coef. = 0.607
- Test Mean IoU: 0.8336
- Test Precision: 0.8354
- Test Recall: 0.7926
Brain Scans Segmentation Results
- Test Loss = 0.2063
- Test Dice Coef. = 0.768
- Test Mean IoU: 0.624
- Test Precision: 0.806
- Test Recall: 0.734
Breast Scans Segmentation Results
In this section it will be demonstrated how all of the models will work together
System Simulation With Brain Scan