2021
DOI: 10.3390/su13031224
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Abstract: As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical imag… Show more

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Cited by 335 publications
(124 citation statements)
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“…Considering the 5400 datasets used for evaluation experimentation, we believe our method shows generalization in relation to skin lesion segmentation. It should also be noted that while deep learning methods have shown good promise in object classification challenges because of their learning ability using feature sets, recent literature reports have suggested that their accuracies in the domain of medical image segmentation need further improvement [75]. Deep learning segmentation methods have been reported to also lack pixel-level accuracy without the application of further processing [76,77].…”
Section: Discussionmentioning
confidence: 99%
“…Considering the 5400 datasets used for evaluation experimentation, we believe our method shows generalization in relation to skin lesion segmentation. It should also be noted that while deep learning methods have shown good promise in object classification challenges because of their learning ability using feature sets, recent literature reports have suggested that their accuracies in the domain of medical image segmentation need further improvement [75]. Deep learning segmentation methods have been reported to also lack pixel-level accuracy without the application of further processing [76,77].…”
Section: Discussionmentioning
confidence: 99%
“…Eventually, it can reduce the amount of intensity variation between one pixel and the other pixels while keeping the sharpness of image edges [ 22 ]. A detailed analysis was conducted in [ 23 ] to show the complexities of the applications of pre-processing techniques in different types of medical images and the need for this process. In this work, Contrast limited adaptive histogram equalization (CLAHE) has been chosen as it operates on small region of the breast image rather than the entire image and apply equalization on each of them.…”
Section: Methodsmentioning
confidence: 99%
“…Note the difference in feature number in contrast to example images from datasets used in different applications, presented in Figure 1b-d. As a side note, the SURF features are presented in Figure 1 Due to the complex (and unique) nature of the medical images, most CNN applications in image processing involve classification [8,9]. Since classification output is discrete (i.e., classes) it is considered less difficult than regression, where output is usually a real number (keypoint positions, segmentation, object detection, etc.).…”
Section: Related Workmentioning
confidence: 99%