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).