Machine Learning and Infrared Thermography for Breast Cancer Detection

Goncalves, Cristiane Sales; Leles, Amanda C. Q.; Oliveira, Lucimara E.; Guimarães, Gilmar; Cunha, Juliano Rodrigues da; Fernandes, Henrique

doi:10.3390/proceedings2019027045

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…Furthermore, features that incorporate shape were used to detect tumor cells using ANNs and an NB classifier. Goncalves et al [ 28 ] discussed an approach to early breast cancer diagnosis. This work followed two different strategies.…”

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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“…We used the multi-class error-correcting output codes (ECOC) model the SVM modelling, which allows classification in more than two classes; and the MATLAB fitcecoc function that creates and adjusts the template for SVM [23]. The Kernel functions considered in the SVM were: Linear, Radial basis function, Gaussian and Polynomial.…”

Section: Model Tuningmentioning

confidence: 99%

“…The receiver operating curve (ROC) is a graph where recall is plotted as a function of 1-specificity. It can more objectively measure the performance of the model itself [23]. The model performance was also evaluated using the area under the ROC, which is denoted the area under curve (AUC).…”

Section: Performance Analysismentioning

confidence: 99%

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Deng

Huang

Yuan

et al. 2021

Laboratory Investigation

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Most of the biomedical datasets, including those of 'omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms.Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN) and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes in rectangular data are poorly understood. Therefore, we aimed to compare these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the Surveillance, Epidemiology, and End Results-18 (SEER-18) database which were diagnosed in 2004 and followed up until December 2016. The outcome was the 6-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, infection and other cause. We included 58 dichotomized features from ~53,000 patients. All analyses were performed using MatLab (version 2018a) and the 10-fold cross validation approach. The accuracy in classifying 6-category cause of death with DT, RF, ANN and SVM was 72.68%, 72.66%, 70.01% and 71.85%, respectively. Based on the information entropy and information gain of feature values, we optimized dimension reduction (i.e. reduce the number of features in models).We found 22 or more features were required to maintain the similar accuracy, while the running time decreased from 440s for 58 features to 90s for 22 features in RF, from 70s to 40s in ANN and from 440s to 80s in SVM. In summary, we here show that RF, DT, ANN and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will significantly increase model's efficiency while maintaining classification accuracy.

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“…In [45], the authors used a technique of segmentation called hot region segmentation method which depends on separating objects from background clearly after applying the technique of k-means clustering which was used to classify colors for Lab mode after the conversion from RGB mode to show the difference between colors and measure that difference. In [46], the proposed method depended on using machine learning techniques in detecting breast cancer by applying three classes of thermal images for a patient, namely, healthy, benign, or malignant. After segmenting the ROI, they applied a combination of feature extraction methods.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

et al. 2020

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Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this paper, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic Salp Swarm Algorithm (CSSA). Although the Salp Swarm Algorithm (SSA) shows superiority in singleobjective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by CSSA. The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. CSSA algorithm is developed based on ten chaotic maps to enhance the original SSA convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to realworld thermal images for the breast area. The results demonstrate that the proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm. Moreover, a dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.

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Machine Learning and Infrared Thermography for Breast Cancer Detection

Cited by 13 publications

References 7 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

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

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