Analysis of Transform-Based Features on Lateral View Breast Thermograms

Jeyanathan, Josephine Selle; Shenbagavalli, A.; Venkatraman, B.; Menaka, M.; Anitha, J.; Albuquerque, Victor Hugo C. de

doi:10.1007/s00034-019-01148-4

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Ak [ 33 ] discussed various approaches of machine learning and applied them to the Wisconsin Diagnostic Breast Cancer (WBCD) dataset, focusing on comparative analysis and data visualization. Jeyanathan et al [ 34 ] extracted features from breast thermograms using wavelet, curvelet, and contourlet transform for breast cancer recognition, achieving an accuracy of 91%, a sensitivity of 87%, and a specificity of 90% using the AdaBoost classifier. Abdar et al [ 35 ] used voting and stacking techniques to construct a two-layer nested ensemble (NE) model with single classifiers (naïve Bayes and BayesNet), which was tested on the WDBC dataset, achieving an accuracy of 98.07%.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

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Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

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Section: Related Workmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

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“…When examining the table closely, it becomes clear that the proposed approach performs better than the others. The proposed method outperforms the ones in reference 42 by 5.4%, 11.1%, and 6.9%, respectively; in connection, 43 by 1.7%, 9.24%, and 2.16%; in reference, 44 by 1.9%, 1.3%, and 16.1%; and in contact, 45 by 5.7%, 5.1%, and 0.9%, respectively.…”

Section: Odmgc Validationsmentioning

confidence: 75%

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Ahmad,

2024

Adaptive Control & Signal

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SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

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“…The cumulative distribution function corresponding to P V ( r a ) is represented as in equation, 33

S_{V} = T ((), r_{a}) = \sum_{j = 0}^{a} P_{V} ((), r_{j}) a = 0, 1, 2, \dots, L - 1

…”

Section: Proposed Methodologymentioning

confidence: 99%

“…where n is the total number of pixels in the image, n a is the number of pixels that have gray level r a , L is the total number of possible gray levels in the image, P V ((r a ) vs r a is called a histogram. The cumulative distribution function corresponding to P V (r a ) is represented as in equation, 33 S V = T r a ð Þ= X a j = 0…”

Section: Self-adaptive Gray Level Histogram Equalization Based On Power Law Transformationmentioning

confidence: 99%

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Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method

Raj¹,

Sundaram²,

Jaya³

2020

Int J Imaging Syst Tech

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The early detection of tumor is necessary to save a number of lives. In women, the temperature of the affected area of the tumor is warmer than the unaffected area; therefore the thermography technique can be used to capture the cancerous breast images with a thermal infrared by identifying the temperature difference between them. Color enhancement of the captured breast image is an important consideration for early detection of tumor at this stage. Therefore, in this paper, we propose a self‐adaptive gray level histogram equalization approach to enhance the color of the IR image for early detection of the tumor. This approach does not require any manual feeding of parameters toward images. The final classification of tumorous and non‐tumorous breast images can obtain through certain procedures, which includes, image acquisition, pre‐processing, segmentation, feature extraction and classification. This paper emphasizes the support vector machine (SVM) technique to classify the tumor IR thermography images. The proposed approach is implemented in MATLAB and the experimental results shows an outstanding color enhancement of IR images and better classification compared to other existing methods such as CLAHE, BIi‐histogram equalization and adaptive histogram equalization. The performance was evaluated by using evaluation metrics such as sensitivity, accuracy, and specificity of thermography breast image by the SVM classifier adapted with various color enhancement approaches are found to be 91.6%, 90%, and 87.5%. This approach helps in medical field for early diagnosis with high reliability.

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Analysis of Transform-Based Features on Lateral View Breast Thermograms

Cited by 8 publications

References 25 publications

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method

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