Abstract
Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.
Highlights
The convolutional neural techniques have an enormous impact on various areas of medical science
The keywords used for article selection include; deep neural architectures, semantic segmentation, medical imaging, image analysis, deep learning applications, computer-aided diagnosis, health AI, multi-modal medical system, and benchmark medical datasets
Each phase contributes to the final prediction so that pre-processing unit enhances the input image provided to the network model to segment the region of interest, followed by a post-processing step to refine the outcome
Summary
The convolutional neural techniques have an enormous impact on various areas of medical science. The authors in [47] have provided a comprehensive review of deep learning-based image segmentation architectures used for general computer vision tasks. Our survey presents an in-depth, comprehensive review of different aspects, including the benchmark dataset, semantic architectures designed explicitly for medical image segmentation, an updated survey of the latest designed techniques, improvement mechanisms developed over time, evaluation metrics, challenges, and potential recommendations to fix highlighted challenges. The keywords used for article selection include; deep neural architectures, semantic segmentation, medical imaging, image analysis, deep learning applications, computer-aided diagnosis, health AI, multi-modal medical system, and benchmark medical datasets. Models that result in an f-measure close to 1 are considered optimal, which means that there are low false positives and false negatives This metric applies to binary as well as multi-class classification and segmentation problems [127]. Mask image ground truth (provided for training and used internally for scoring validation and test phases) data were generated using several techniques, but all data were reviewed and curated by practicing dermatologists with expertise in dermoscopy [138]
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