An Enhanced Breast Lesion Segmentation using DeepLabv3+ for Improved Ultrasound Diagnosis

Abstract

Breast cancer is one of the leading causes of death in women and early diagnosis and correct diagnosis is important. Although ultrasound imaging is widely used due to its non-invasive nature and is low cost, suitable for real time clinical assessment, lesion segmentation is challenging because of speckle noise, low contrast, shadowing, fuzzy boundaries and variations in lesion size and shape. This paper proposes an improved DeepLabv3+ segmentation framework using EfficientNetB4 as the encoder to localize the breast lesions in ultrasound images. It was chosen because of its ability to achieve a high accuracy to computation ratio via compound scaling and can also be used to generate rich multi-scale feature representations, using fewer parameters than its heavier counterparts. The public Breast Ultrasound Images (BUSI) dataset is subjected to image resizing, normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), synchronized augmentation and pixel-wise mask learning during the proposed pipeline. CLAHE has the effect of increasing the contrast of the lesion boundary region in it and decreasing the ambiguity of the boundaries prior to feature extraction which enhances the interpretability of the segmentation output. Experimental analysis shows an accuracy of 94.57%, precision of 84.11%, recall of 42.59% and F1-score of 56.55%. While recall is still moderate for difficult lesions, the model shows stable convergence, high pixel-level accuracy and clinically useful lesion localization. The new study also provides more insight into the split of the data set and the composition of the data set and the comparison with other latest segmentation models.