Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

• Published in: PloS one Vol. 16; no. 5; p. e0251899
• Main Authors: Badawy, Samir M., Mohamed, Abd El-Naser A., Hefnawy, Alaa A., Zidan, Hassan E., GadAllah, Mohammed T., El-Banby, Ghada M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.05.2021; Public Library of Science (PLoS)
• Subjects: Analysis; Artificial intelligence; Automation; Batch processing; Breast cancer; Classification; Computer and Information Sciences; Conditional random fields; Datasets; Deep learning; Diagnosis; Electronic engineering; Electronics; Engineering; Engineering and Technology; Evaluation; Feasibility studies; Fuzzy algorithms; Fuzzy logic; Fuzzy systems; Image processing; Image segmentation; Industrial electronics; Learning algorithms; Lung cancer; Lung diseases; Machine learning; Mammography; Medical imaging; Medicine and Health Sciences; Neural networks; Performance evaluation; Research and Analysis Methods; Semantic segmentation; Semantics; Tissues; Tumors; Ultrasonic imaging; Ultrasound; Egypt; Cairo Egypt
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0251899

Computer aided diagnosis ( CAD ) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic ( FL ) and deep learning ( DL ) for automatic semantic segmentation ( SS ) of tumors in breast ultrasound ( BUS ) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network ( CNN ) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy ( GA ), mean Jaccard Index (mean intersection over union ( IoU )), and mean BF (Boundary F1) Score. In the batch processing mode : quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45 % GA instead of 86.08 % without applying fuzzy preprocessing step, 78.70 % mean IoU instead of 49.61 %, and 68.08 % mean BF score instead of 42.63 %. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest ( ROI ) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).