Fig 1.
Illustrative Flowchart for the proposed framework.
Table 1.
Semantic segmentation evaluation metrics for 200 benign BUS images before and after applying fuzzy enhancement (based on batch images’ processing).
Table 2.
Semantic segmentation evaluation metrics for 200 malignant BUS images before and after applying fuzzy enhancement (based on batch images’ processing).
Table 3.
Average quantitative evaluation metrics (based on batch images’ processing) over for 400 BUS images over 8 CNN based SS models (average Tables 1 and 2).
Table 4.
Semantic segmentation evaluation metrics for 200 benign BUS images before and after applying fuzzy enhancement (based on one by one image processing).
Table 5.
Semantic segmentation evaluation metrics for 200 malignant BUS images before and after applying fuzzy enhancement (based on one by one image processing).
Table 6.
Average quantitative evaluation metrics (based on one by one image processing) over for 400 BUS images over 8 CNN based SS models (average Tables 4 and 5).
Fig 2.
Illustrative chart for the average global accuracy results in percent for eight CNNs based SS over 400 BUS images presented in Table 3 (batch processing).
Fig 3.
Illustrative chart for the average mean IoU (Jaccard Index) results in percent for eight CNNs based SS over 400 BUS images presented in Table 3 (batch processing).
Fig 4.
Illustrative chart for the average mean BF (Boundary F1) Score results in percent for eight CNNs based SS over 400 BUS images presented in Table 3 (batch processing).
Fig 5.
Eight samples from 200 BUS benign images’ results for ResNet18 (batch processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.
Fig 6.
Eight samples from 200 BUS benign images’ results for U-Net (batch processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.
Fig 7.
Eight samples from 200 BUS malignant images’ results for ResNet18 (batch processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.
Fig 8.
Eight samples from 200 BUS malignant images’ results for U-Net (batch processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.
Fig 9.
Illustrative chart for the average global accuracy results in percent for eight CNNs based SS over 400 BUS images presented in Table 6 (one by one processing).
Fig 10.
Illustrative chart for the average mean IoU (Jaccard Index) results in percent for eight CNNs based SS over 400 BUS images presented in Table 6 (one by one processing).
Fig 11.
Illustrative chart for the average mean BF (Boundary F1) Score results in percent for eight CNNs based SS over 400 BUS images presented in Table 6 (one by one processing).
Fig 12.
Eight samples from 200 BUS benign images’ results for ResNet18 (one by one processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.
Fig 13.
Eight samples from 200 BUS benign images’ results for U-Net (one by one processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.
Fig 14.
Eight samples from 200 BUS malignant images’ results for ResNet18 (one by one processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.
Fig 15.
Eight samples from 200 BUS malignant images’ results for U-Net (one by one processing).
Each sample consist of four images: 1st the original image from the prepared small dataset, 2nd the segmentation results without applying fuzzy enhancement, 3rd the segmentation result after applying fuzzy based enhancement to the input image, and 4th the original ground truth.