An Extensive Survey on Various Tumor Detection in Histopathological Images Using Deep Learning Techniques

DOI

10.2991/978-94-6463-252-1_13How to use a DOI?

Keywords

Deep Learning; Filtering Techniques; Histopathological images; Tumor detection; Whole slide images

Abstract

Nowadays Biomedical Image Processing is the most developing field and its demand also more in various growth of several applications. Research into these medical technologies has many features and applications. It includes a variety of imaging modalities that can analyze, enhance, and display images from X-ray, ultrasound, MRI, nuclear medicine, and optical imaging techniques. Those features extracted from suspect regions of the images will help them find the place where the tumor is present in real-time and is useful for speeding up the treatment process. Over the last few years, a tomographic image shows poor qualities which are not specific to metastasis. It is expensive compared with Histopathological images which provide high-resolution images at a low cost. The main motivation to select a Histopathological image over a tomographic image is that a tomographic image is a preoperative diagnosis, whereas Histopathological images provide both a preoperative diagnosis and provide a prognostic assessment of post-disease treatment (surgery) for effective decision-making regarding further treatment. Moreover, in artificial intelligence, deep learning algorithms have become the successors of pathology image analysis for tumor area segmentation, identification, metastasis detection and patient prediction. So, depending upon the development of deep learning advancements in Histopathological Images, a comparison of deep learning algorithms is surveyed and the deep learning algorithms deal with high-level features in detecting tumors using Histopathological Images.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Monika Subramanian
AU  - Nagarajan Ganesan
AU  - SathishKumar Balasubramaniyan
PY  - 2023
DA  - 2023/11/09
TI  - An Extensive Survey on Various Tumor Detection in Histopathological Images Using Deep Learning Techniques
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 105
EP  - 118
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_13
DO  - 10.2991/978-94-6463-252-1_13
ID  - Subramanian2023
ER  -