Wavelet based thermogram analysis for breast cancer detection

Pramanik, Sourav; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1109/isacc.2015.7377343

Cited by 55 publications

(14 citation statements)

References 14 publications

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“…The following parameters are adopted to evaluate the performance, where Ture positives (TP) means the number of abnormalities diagnosed in patients, Ture negative (TN) is the number of normal people diagnosed as normal, False positives (FP) indicates the number of healthy people misdiagnosed as abnormal and False negative (FN) is equivalent to the number of patients missed. The metric used in this paper refers to [12]. The results of breast cancer detection of KNN can be seen in Fig.…”

Section: Diagnosis Resultsmentioning

confidence: 99%

A portable breast cancer detection system based on smartphone with infrared camera

Ma¹,

Shang²,

Chen³

et al. 2019

Vib. proced.

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The traditional detection methods have the disadvantages of radiation exposure, high cost, and shortage of medical resources, which restrict the popularity of early screening for breast cancer. An inexpensive, accessible, and friendly way to detect is urgently needed. Infrared thermography, an emerging means to breast cancer detection, is extremely sensitive to tissue abnormalities caused by inflammation and vascular proliferation. In this work, combined with the temperature and texture features, we designed a breast cancer detection system based on smart phone with infrared camera, achieving the accuracy of 99.21 % with the k-Nearest Neighbor classifier. We compared the diagnostic results of the low resolution, originated from the phone camera, with the high resolution of the conventional infrared camera. It was found that the accuracy and sensitivity decreased slightly, but both of them were over than 98 %. The proposed breast cancer detection system not only has excellent performance but also dramatically saves the detection cost, and its prospect will be fascinating.

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Section: Diagnosis Resultsmentioning

confidence: 99%

A portable breast cancer detection system based on smartphone with infrared camera

Ma¹,

Shang²,

Chen³

et al. 2019

Vib. proced.

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show abstract

“…To improve classification metrics, IR data could be complemented with information collected from other imaging modalities and/or biological tests [9,26,33,64,70]. The availability of a larger data sample is also mentioned by several studies across the different pathologies, in order to perform more complete testing and ease the implementation of such methodologies in daily practices [9,16,25,26,31,44,46,[52][53][54]58,60,63,65,67]. Apart from the mentioned suggestions, the implementation of parameter optimization during the construction of the learner may yield better classification results, as well as the use of strategies to deal with potential class imbalance problems.…”

Section: Discussionmentioning

confidence: 99%

“…A fuzzy model based on C-means clustering by Lashkari et al showed accuracy values of 75% for the screening of suspicious breast areas, a lower performance when compared to the supervised AdaBoost algorithm developed in their previous work (88%) [23,24]. Other strategies to improve neural networks' performance in breast cancer classification refer to the extraction of features with wavelet transform [25], or to numerical simulations conducted on various breast tissue composition models [26].…”

Section: Breast Cancermentioning

confidence: 99%

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

2021

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Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.

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“…In [39], the researchers, however, calculated the Initial Feature point Image (IFI) for each segmented breast thermogram by applying a Discrete Wavelet Transform (DWT). Then 15 types of features were extracted before being inserted into the Artificial Neural Network.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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Wavelet based thermogram analysis for breast cancer detection

Cited by 55 publications

References 14 publications

A portable breast cancer detection system based on smartphone with infrared camera

A portable breast cancer detection system based on smartphone with infrared camera

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

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