A Novel Model for Smart Breast Cancer Detection in Thermogram Images

Kazerouni, Iman Abaspur; Zadeh, Hossein Ghayoumi; Haddadnia, Javad

doi:10.7314/apjcp.2014.15.24.10573

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…This approach applied for 400 thermographic images. As the result, the highest precision value was 99.33% for (2D)2PCA and 90.86 % for 2DPCA and 82.98 for PCA (Kazerouni et al, 2014).…”

Section: Experiences In Breast Cancer Diagnosis Through Image Processingmentioning

confidence: 86%

“…An important factor to reduce mortality rate and increase long term survival in breast cancer is early detection and effective treatment (Guo, 2010;Choudhari et al, 2012;Tripathy, 2013;Kazerouni et al, 2014;Mohaghegh et al, 2015;Vithana et al, 2015). Also treatment of breast cancer is very expensive, therefore, early detection leads to reduce personal, health and socioeconomical complication (Zadeh et al, 2012;Kulakci et al, 2015).…”

Section: Breast Cancer Diagnosis Through Image Processingmentioning

confidence: 99%

See 1 more Smart Citation

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Mohammadzadeh

Safdari²,

Ghazisaeidi³

et al. 2015

Asian Pacific Journal of Cancer Prevention

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Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

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“…This approach applied for 400 thermographic images. As the result, the highest precision value was 99.33% for (2D)2PCA and 90.86 % for 2DPCA and 82.98 for PCA (Kazerouni et al, 2014).…”

Section: Experiences In Breast Cancer Diagnosis Through Image Processingmentioning

confidence: 86%

Section: Breast Cancer Diagnosis Through Image Processingmentioning

confidence: 99%

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Mohammadzadeh

Safdari²,

Ghazisaeidi³

et al. 2015

Asian Pacific Journal of Cancer Prevention

View full text Add to dashboard Cite

show abstract

“…Using a different retrospective database, Kazerouni et al ( 2014 ) used a support vector machine with an RBF kernel for image retrieval and used their MATLAB model on 400 thermographic images captured and collected by Hakim Sabzevari Medical Imaging Group in Iran. The sensitivity and specificity of the model were 100 and 98%, respectively.…”

Section: Discussionmentioning

confidence: 99%

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital

Bansal

Collison²,

Krishnan³

et al. 2023

Front. Artif. Intell.

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ObjectiveArtificial intelligence-enhanced breast thermography is being evaluated as an ancillary modality in the evaluation of breast disease. The objective of this study was to evaluate the clinical performance of Thermalytix, a CE-marked, AI-based thermal imaging test, with respect to conventional mammography.MethodsA prospective, comparative study performed between 15 December 2018 and 06 January 2020 evaluated the performance of Thermalytix in 459 women with both dense and nondense breast tissue. Both symptomatic and asymptomatic women, aged 30–80 years, presenting to the hospital underwent Thermalytix followed by 2-D mammography and appropriate confirmatory investigations to confirm malignancy. The radiologist interpreting the mammograms and the technician using the Thermalytix tool were blinded to the others' findings. The statistical analysis was performed by a third party.ResultsA total of 687 women were recruited, of whom 459 fulfilled the inclusion criteria. Twenty-one malignancies were detected (21/459, 4.6%). The overall sensitivity of Thermalytix was 95.24% (95% CI, 76.18–99.88), and the specificity was 88.58% (95% CI, 85.23–91.41). In women with dense breasts (n = 168, 36.6%), the sensitivity was 100% (95% CI, 69.15–100), and the specificity was 81.65% (95% CI, 74.72–87.35). Among these 168 women, 37 women (22%) were reported as BI-RADS 0 on mammography; in this subset, the sensitivity of Thermalytix was 100% (95% CI, 69.15–100), and the specificity was 77.22% (95% CI, 69.88–83.50).ConclusionThermalytix showed acceptable sensitivity and specificity with respect to mammography in the overall patient population. Thermalytix outperformed mammography in women with dense breasts and those reported as BI-RADS 0.

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“…Using a different retrospective database, Kazerouni et al [27] used a support vector machine with an RBF kernel for image retrieval and used their MATLAB model on 400 thermographic images captured and collected by Hakim Sabzevari Medical Imaging Group in Iran. The sensitivity and specificity of the model were 100% and 98%, respectively.…”

Section: Discussionmentioning

confidence: 99%

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital

Bansal

Collison²,

Aggarwal

2022

Preprint

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Objective: Artificial intelligence enhanced breast thermography is being evaluated as an ancillary modality in the evaluation of breast disease. The objective of this study is to evaluate clinical performance of Thermalytix, a CE marked AI based thermal imaging test in comparison with conventional mammography. Methods: A prospective, comparative study performed between 15 December 2018 and 06 January 2020 evaluated the performance of Thermalytix in 459 women with both dense and non-dense breast tissue. Both symptomatic and asymptomatic women, aged 30 to 80 years, presenting to hospital underwent Thermalytix followed by 2 D mammography and appropriate confirmatory investigations to confirm malignancy. The radiologist interpreting mammograms and technician using Thermalytix tool were blinded to the others' findings. The statistical analysis was performed by a third party. Results: 687 women were recruited and 459 women fulfilled inclusion criteria. 21 malignancies were detected (21/459, 4.6%). Overall sensitivity of Thermalytix was 95.24% (95% CI, 76.18-99.88) and specificity 8858% (95% CI, 85.23-91.41). In those with dense breasts (n168, 36.6%), sensitivity was 100% (95% CI, 69.15-100) and specificity 81.65% (95% CI, 74.72-87.35). Among these 168 women, 37 women (22%) were reported as BI-RADS 0 on mammograms; in this subset, the sensitivity of Thermalytix was 100% (95% CI, 69.15-100) and specificity 77.22% (95% CI, 69.88-83.50). Conclusion: Thermalytix has shown acceptable sensitivity and specificity when compared with mammography in the overall patient population. Thermalytix outperformed mammography in dense breasts and those reported as BI-RADS 0.

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A Novel Model for Smart Breast Cancer Detection in Thermogram Images

Cited by 4 publications

References 12 publications

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital

A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital

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