A Survey on Medical Image Segmentation in Deep Learning

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@article{1704526,
      author = {Shubhranshu Gupta, Karthik Panicker, Jayant Bhowmick},
      title = {A Survey on Medical Image Segmentation in Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {651-658},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704526.pdf},
      abstract = {Lung cancer is the leading cause of death in most countries around the world. Radiologists usually segment images manually, which takes time and results in less precise results. Since the early discovery of tumors can assist radiologists in determining their nature, sort, and mode of therapy, tumor detection and segmentation from CT Scan images is an important area of study. For experts, automated segmentation facilitates faster data analysis. Deep learning is a machine learning algorithm technique. In deep learning, data transformation takes the form of layers. This paper proposes deep learning, which is based on convolutional neural networks and is mostly used for image processing. The recommended Lung tumor segmentation is being developed utilizing cutting-edge technology such as deep learning algorithms, and CNN, to segment and identify tumors and decide which deep learning approach is most suited for segmenting. Two examples of deep learning techniques are UNET and UNETR. These techniques are frequently applied to segmentation-related problems. The dataset for the segmentation of lung tumors is made available through the NSCLC and Decathlon challenge and consists of lung CT scans.},
      keywords = {Convolutional Neural Network, Deep Learning, Segmentation, Transformer.},
      month = {May},
  }