Can Deep Learning Do Everything in AI? An Exploration of Simple Examples in Image Segmentation (original) (raw)
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Segmentation of medical images using deep learning has provided stateof-the-art performances in this area of work. With the availability of large digital datasets and access to powerful GPUs, deep learning has transformed our world. We are now able to make computers mimic and replicate the functions of the human mind simply by providing enough data and computing the problem. Deep learning has a huge potential for medical image analysis and now it has been firmly established as a robust tool in image segmentation. This paper addresses the six popular methods that have employed deep-learning techniques for the segmentation of medical images which play a massive impact in the medical healthcare industry and in turn make a contributing role towards the concept of smart cities. A comparative study on these deep learning-based segmentation techniques will provide a researcher working in the field of medical imaging to explore further in this area for higher accuracy and better results.
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IJERT-A Survey on Semantic Segmentation using Deep Learning Techniques
International Journal of Engineering Research and Technology (IJERT), 2021
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Image segmentation is crucial for various research areas. Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging. With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation. This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation. First, segmentation models are categorized based on their model architecture and primary working principle. Then, CNN categories are described, and various models are discussed within each category. Compared with other existing surveys, several applications with multiple architectural adaptations are discussed within each category. A comparative summary is included to give the reader insights into utilized architectures in different applications and datasets. This study focuses on medical image segmentation applications, where the most widely used architectures are illustrated, and other promising models are suggested that have proven their success in different domains. Finally, the present work discusses current limitations and solutions along with future trends in the field.
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Medical image segmentation using deep learning: A survey
IET Image Processing, 2022
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.
IEEE Access
Machine learning and deep learning algorithms are widely used in computer science domains. These algorithms are mostly used for classification and regression problems in almost every field of life. Semantic segmentation is an instantly growing research topic in the last few decades that refers to the association of each pixel in the image to the class it belongs. This paper illustrates the systematic survey of advanced research in the field of semantic segmentation till date. This study provides the brief knowledge about the latest proposed methods in the domain of semantic segmentation. The proposed study comprehends the concepts, techniques, tool, and results of different research frameworks proposed in the context of semantic segmentation. This study discusses the latest research papers in which machine learning and deep learning techniques are exploited for semantic segmentation and published between 2016 and 2021. The systematic literature review collected from seven different article libraries including ACM digital Library, Google Scholar, IEEE Xplore, Science Direct, Google Books, Refseek and Worldwide Science. For assuring the quality of the paper those papers are selected which have several citations on standardized platforms. Most of the studies used COCO, PASCAL, Cityscapes and CamVid dataset for training and validation of the machine learning and deep learning models. The results of the selected research articles are collected in the form of accuracy, mIoU value, F1 score, precision, and recall. In this study, we also conclude that most of the semantic segmentation studies use ResNet as the backbone of the architecture and none of the researchers used ensemble learning methods for semantic segmentation that is the loophole of the selected studies.