Leveraging Deep Learning Techniques to Obtain Efficacious Segmentation Results


  • Joy Purohit Department of Computer Engineering and Information Technology, Veermata Jijabai Technological Institute, India
  • Rushit Dave Department of Computer Information Science, Minnesota State University, USA




deep learning, image segmentation, medical imaging, iris recognition, pedestrian detection, autonomous driving


Image segmentation is a critical task in the field of computer vision. In the past, traditional segmentation algorithms were frequently used to tackle this problem but had various shortcomings. However, the advent of deep learning has revolutionized this field, leading to the development of novel image segmentation algorithms. This paper presents a comprehensive overview of deep learning-based models applied to medical imaging, iris recognition, pedestrian detection, and autonomous driving. The study encompasses various techniques, such as convolutional neural networks, fully convolutional neural networks, encoder–decoder architectures, multi-scale approaches, attention mechanisms, and image transformers. Moreover, this paper evaluates the performance of these models on relevant datasets, providing insightful recommendations for researchers to integrate promising techniques into their work for specific applications. The discussion also explores the challenges, constraints, and potential research directions in these domains.


Received: 16 June 2023 | Revised: 19 July 2023 | Accepted: 25 July 2023


Conflicts of Interest

The authors declare that they have no conflicts of interest in this work.




How to Cite

Purohit, J., & Dave, R. (2023). Leveraging Deep Learning Techniques to Obtain Efficacious Segmentation Results. Archives of Advanced Engineering Science, 1(1), 11–26. https://doi.org/10.47852/bonviewAAES32021220