Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

Alfayez, Fayez; El-Soud, Mohamed W. Abo; Gaber, Tarek

doi:10.3390/app10020551

Cited by 42 publications

(17 citation statements)

References 22 publications

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“…Finally the extracted features are used for the training of various conventional machine learning classifiers like Naï ve Bayes (NB), Support vector machine (SVM), K nearest neighbor (KNN), Random Forest (RF), Decision tree, AdaBoost etc. [24][25][26][27][28][29] to classify the breast thermal images into either normal or abnormal cases. Mostly the accuracies of these machine learning based approaches varies in the range from 92% to 97%.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Multi-View Breast Cancer Detection: A Multi-View Concatenated Infrared Thermal Images Based Breast Cancer Detection System Using Deep Transfer Learning

Tiwari¹,

Dixit²,

Gupta³

2021

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This paper simply presents a fully automated breast cancer detection system as “Deep Multi-view Breast cancer Detection” based on deep transfer learning. The deep transfer learning model i.e., Visual Geometry Group 16 (VGG 16) is used in this approach for the correct classification of Breast thermal images into either normal or abnormal. This VGG 16 model is trained with the help of Static as well as Dynamic breast thermal images dataset consisting of multi-view, single view breast thermal images. These Multi-view breast thermal images are generated in this approach by concatenating the conventional left, frontal and right view breast thermal images taken from the Database for Mastology Research with Infrared image for the first time in order to generate a more informative and complete thermal temperature map of breast for enhancing the accuracy of the overall system. For the sake of genuine comparison, three other popular deep transfer learning models like Residual Network 50 (ResNet50V2), InceptionV3 network and Visual Geometry Group 19 (VGG 19) are also trained with the same augmented dataset consisting of multi-view as well as single view breast thermal images. The VGG 16 based Deep Multi-view Breast cancer Detect system delivers the best training, validation as well as testing accuracies as compared to their other deep transfer learning models. The VGG 16 achieves an encouraging testing accuracy of 99% on the Dynamic breast thermal images testing dataset utilizing the multi-view breast thermal images as input. Whereas the testing accuracies of 95%, 94% and 89% are achieved by the VGG 19, ResNet50V2, InceptionV3 models respectively over the Dynamic breast thermal images testing dataset utilizing the same multi-view breast thermal images as input.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Multi-View Breast Cancer Detection: A Multi-View Concatenated Infrared Thermal Images Based Breast Cancer Detection System Using Deep Transfer Learning

Tiwari¹,

Dixit²,

Gupta³

2021

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“…However, the outcome of neural network proved that the adopted machine learning model is effective and efficient for both sets of input data as well as it confirmed that it can be more vigorous than the BCRAT. AlFayez et al [22], introduced a thermogrambased breast cancer detection approach. At first, a preprocessing of image was achieved by utilizing top-hat transform, homomorphic filtering in addition to an adaptive histogram equalization.…”

Section: Related Workmentioning

confidence: 99%

The Use of Modified K-Means Algorithm to Enhance the Performance of Support Vector Machine in Classifying Breast Cancer

Al-Yaseen¹,

Jehad²,

Abed³

et al. 2021

IJIES

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Breast cancer has been recently considered as one of the broadly spread diseases that causes death among women. Early disease diagnosis is a critical aim in building the treatment policies and is extremely related to safety of patient. Therefore, there is a necessity for computer aided detection (CAD) in order to provide accurate and rapid diagnosis for breast cancer. Recently, many classification models utilizing machine learning approaches have been adopted and modified to diagnose breast cancer disease. Moreover, the performance of each model depends on different compositions such as the number and type of data features and the parameters of model. In order to enhance the performance of classification model, this research proposes a model using modified K-means algorithm to create a new training dataset of breast cancer which can highly improve the performance of support vector machine model. A modified K-means algorithm is also proposed to build a high quality training dataset that contributes significantly to reduce the training time of classifiers, and improve the performance of classifier. The proposed model handles the noise and irregularity in data and produce high quality dataset which represents all the cases of disease. The two recognized datasets Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) have been used to examine and appraise the performance of the proposed model. The experimental results show that the proposed model has a significant performance compared to other previous works and with accuracy level of 98.067%, sensitivity of 100%, specificity of 94.811%, precision of 97.011% and finally with area under the curve related to the receiver operating characteristic of 97.406%.

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“…In the segmentation step, they used a combination of binary masking, k-means clustering, and the signature boundary for the feature extraction step. In another research [42], two methods of classification were used, namely, Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM). In [43], the authors proposed a new automatic segmentation method that consisted of preprocessing, segmentation, and separation for an area of interest and then, segmenting ROI to both right and left breasts.…”

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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Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

Cited by 42 publications

References 22 publications

Deep Multi-View Breast Cancer Detection: A Multi-View Concatenated Infrared Thermal Images Based Breast Cancer Detection System Using Deep Transfer Learning

Deep Multi-View Breast Cancer Detection: A Multi-View Concatenated Infrared Thermal Images Based Breast Cancer Detection System Using Deep Transfer Learning

The Use of Modified K-Means Algorithm to Enhance the Performance of Support Vector Machine in Classifying Breast Cancer

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

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