Breast Cancer Detection and Classification Using Thermography: A Review

Ibrahim, Abdelhameed; Mohammed, Shaimaa; Ali, Hesham

doi:10.1007/978-3-319-74690-6_49

Cited by 19 publications

(12 citation statements)

References 29 publications

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“…Even though CXR is more accessible around the world hospitals, chest CT scan is more sensitive than CXR for early detection of COVID-19 disease changes, as well as for staging of the disease and monitoring progression [4] , [5] . CT images, on the other hand, are considered a powerful analysis tool [6] , [7] widely applied to biomedical imaging [8] and clinical diagnosis [9] and provide non-destructive 3D visualization of internal structures. However, the features of the community-acquired bacterial pneumonia is difficult to classify as the COVID-19 [10] .…”

Section: Introductionmentioning

confidence: 99%

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

Aslan

Ünlerşen

Sabancı

et al. 2021

Applied Soft Computing

250

153

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Section: Introductionmentioning

confidence: 99%

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

Aslan

Ünlerşen

Sabancı

et al. 2021

Applied Soft Computing

250

153

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“…Level Co-occurrence Matrix (GLCM) texture-based feature is employed for classi ication by (Gautam et al, 2018;Beura et al, 2015;Pratiwi et al, 2015). Cancer is predicted on the basis of change in temperature between the breasts, the thermograms are classi ied into normal and abnormal based on SVM classi ication (Ibrahim et al, 2018). Deep neural networks and Recursive Feature Elimination were used for classi ication (Karthik et al, 2018).…”

Section: Figure 2: Automated Diagnosis System For Detection Of Breastmentioning

confidence: 99%

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

N¹,

Rao²

2019

ijrps

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Breast cancer is the second leading cause of death for women everywhere in the world. Since the reason behind the disease remains unknown, early detection and diagnosis is the key challenge for breast cancer control. In this work, mammogram images are initially subject to pre-processing using Laplacian ilter for enhancement of tumour regions, Gaussian mixture model, Gaussian kernel FCM, Otsu global thresholding and FCM technique are employed for segmentation. Further, the ef iciency of segmentation techniques is analyzed by classifying the samples into benign, malignant and healthy using Gray Level Co-occurrence Matrix (GLCM) features. Linear discriminant analysis classiier is used a combination based on which ef iciency used for classi ication of mammograms. Ensemble methods are evaluated. The ef iciency has resulted in better accuracy with the ensemble-based method. The experimentation is conducted in the mini MIAS database of mammograms, and the ef iciency of the linear discriminant analyzer is found to be 89.19% for GKFCM, 83.78% with Otsu and 78.38% with FCM method with GLCM features.

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“…However, it shows a dependency on tumor size, palpability, breast density, tools' quality, physician's expertise who performs the procedure, and interpreting the image [2,5,[20][21][22]. Magnetic resonance imaging (MRI) is also an alternative imaging modality, which can identify early breast cancer in the place where conventional imaging fails to detect the abnormalities [23,24]. Ahen et al (2014) concluded that the high costs and low specificity of MRI limits the popularity of MRI for annual screening for high-risk women [25].…”

Section: Introductionmentioning

confidence: 99%

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

2020

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Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3–81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.

show abstract

Breast Cancer Detection and Classification Using Thermography: A Review

Cited by 19 publications

References 29 publications

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

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