Abstract
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).
Highlights
Breast cancer is regarded as the second common cancer globally after lung cancer, the fifth common reason for cancer death [1]
We introduce an automatic semantic segmentation (SS) approach for batch processing by combining a Fuzzy method for contrast enhancement using an intensification operator as a preprocessing enhancement step before starting a known Convolutional neural network (CNN) based SS scheme
A modest dataset named MT_Small_Dataset has been adjusted and arranged for 1200 images: 400 adjusted to size 128 by 128 by 3 and the same 400 after applying fuzzy based contrast enhancement and 400 image ground truth adjusted in gray level [0 255], size (128 by 128), and have two classes “1” represents normal tissue and “2” represents cancerous tissue to be appropriate for evaluating the most known CNN based semantic segmentation output images using MATLAB [32]
Summary
Breast cancer is regarded as the second common cancer globally after lung cancer, the fifth common reason for cancer death [1]. We introduce an automatic SS approach for batch processing by combining a Fuzzy method for contrast enhancement using an intensification operator as a preprocessing enhancement step before starting a known CNN based SS scheme. A modest dataset named MT_Small_Dataset (based on the 800 images taken from [31]) has been adjusted and arranged for 1200 images: 400 adjusted to size 128 by 128 by 3 and the same 400 (size 128 by 128 by 3) after applying fuzzy based contrast enhancement and 400 image ground truth adjusted in gray level [0 255], size (128 by 128), and have two classes “1” represents normal tissue and “2” represents cancerous tissue to be appropriate for evaluating the most known CNN based semantic segmentation output images using MATLAB [32].
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