A review on Suspicious-Regions Segmentation Methods in Breast Thermogram Image

Dihmani, Hanane; Bouattane, Omar; Grief, Ouafae Serrar

doi:10.1109/iraset52964.2022.9738265

Cited by 4 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies [9][10][11] conducted systematic reviews on various aspects of breast cancer detection, including the use of thermography with artificial intelligence, advances in CNN models, and segmentation techniques. These reviews highlighted the need for more accurate and efficient detection methods using advanced technologies such as Deep Learning.…”

Section: Systematic Literature Review (Slr)mentioning

confidence: 99%

A systematic review of breast cancer detection on thermal images

Aqil Aqthobirrobbany,

Dian Nova Kusuma Hardani,

Indah Soesanti

et al. 2023

CST

View full text Add to dashboard Cite

Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.

show abstract

Section: Systematic Literature Review (Slr)mentioning

confidence: 99%

A systematic review of breast cancer detection on thermal images

Aqil Aqthobirrobbany,

Dian Nova Kusuma Hardani,

Indah Soesanti

et al. 2023

CST

View full text Add to dashboard Cite

show abstract

Generative adversarial network: a statistical-based deep learning paradigm to improve detecting breast cancer in thermograms

Shojaedini,

Abedini,

Monajemi

2023

Med Biol Eng Comput

View full text Add to dashboard Cite

A New method for promote the performance of deep learning paradigm in diagnosing breast cancer: improving role of fusing multiple views of thermography images

2022

View full text Add to dashboard Cite

Purpose Breast cancer is one of the deadliest cancers among women worldwide which its early detection may significantly reduce its mortality rate. Thermgraphy is a new, non-invasive, non-painful, and low-cost modality that detects abnormalities by detecting heat from the breast surface. Method Recent research has applied deep learning to early breast cancer diagnosis via thermography, using only the frontal view of thermograms. We combine several views of thermal images to improve the performance of pre-trained deep learning architectures in this article. This goal is achieved by combining frontal-45 data with lateral-45 and lateral45 thermograms to construct a detection model that utilizes transfer learning. Result Research in this area uses the Database for Mastology Research (DMR) with infrared images. In this study, transfer based deep learning methods are demonstrated to be effective in fusing several views of thermograms to diagnose breast cancer in a manner that can result in a sensitivity increase of 2-15 percent and a specificity increase of 2-30 percent compared to other deep learning-based or handcrafted schemes. Conclusion Using multiple views of thermograms and transfer learning, this paper proposes a method for improving breast cancer diagnosis. Using methods based on deep learning and methods based on hand-crafted features, we evaluated the performance of the proposed model. Using the obtained results as a basis for future research, the proposed design can be improved and developed as a valid approach in interpreting breast thermography images.

show abstract

A review on Suspicious-Regions Segmentation Methods in Breast Thermogram Image

Cited by 4 publications

References 39 publications

A systematic review of breast cancer detection on thermal images

A systematic review of breast cancer detection on thermal images

Generative adversarial network: a statistical-based deep learning paradigm to improve detecting breast cancer in thermograms

A New method for promote the performance of deep learning paradigm in diagnosing breast cancer: improving role of fusing multiple views of thermography images

Contact Info

Product

Resources

About