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Open accessJournal ArticleDOI: 10.1080/21681163.2020.1824685

A CNN-based methodology for breast cancer diagnosis using thermal images

04 Mar 2021-Computer methods in biomechanics and biomedical engineering. Imaging & visualization (Informa UK Limited)-Vol. 9, Iss: 2, pp 131-145
Abstract: A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsi...

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Topics: Mammography (68%), Breast cancer (59%)
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Open accessJournal ArticleDOI: 10.1109/ACCESS.2020.3012292
Deepika Kumar1, Nikita Jain1, Aayush Khurana1, Sweta Mittal1  +3 moreInstitutions (4)
27 Jul 2020-IEEE Access
Abstract: Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells’ images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model’s performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

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9 Citations


Open accessJournal ArticleDOI: 10.3390/BIOS10110164
31 Oct 2020-Biosensors
Abstract: 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.

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Topics: Breast cancer (60%), Mammography (58%), Breast cancer screening (56%) ... show more

7 Citations


Journal ArticleDOI: 10.1007/S10916-020-01581-Y
Aayesha Hakim1, R. N. Awale1Institutions (1)
Abstract: Breast cancer is not preventable To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea Delay in diagnosis of this disease causes 60% of deaths Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body Thermography as a screening tool was first introduced and well accepted in 1956 However, a study in 1977 found that it lagged behind other screening tools and is subjective Soon after, its use was discontinued This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer Different performance metrics like accuracy, specificity, and sensitivity have also been examined This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women

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Topics: Breast cancer (60%)

6 Citations


Open accessJournal ArticleDOI: 10.3390/ELECTRONICS9111810
02 Nov 2020-Electronics
Abstract: The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.

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Topics: Cross-validation (54%)

2 Citations


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47 results found


Open accessJournal ArticleDOI: 10.3322/CAAC.21492
Abstract: This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions There will be an estimated 181 million new cancer cases (170 million excluding nonmelanoma skin cancer) and 96 million cancer deaths (95 million excluding nonmelanoma skin cancer) in 2018 In both sexes combined, lung cancer is the most commonly diagnosed cancer (116% of the total cases) and the leading cause of cancer death (184% of the total cancer deaths), closely followed by female breast cancer (116%), prostate cancer (71%), and colorectal cancer (61%) for incidence and colorectal cancer (92%), stomach cancer (82%), and liver cancer (82%) for mortality Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality) Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts CA: A Cancer Journal for Clinicians 2018;0:1-31 © 2018 American Cancer Society

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Topics: Cancer registry (78%), Cancer (72%), Breast cancer (63%) ... show more

39,828 Citations


Open accessProceedings ArticleDOI: 10.1109/CVPR.2017.195
François Chollet1Institutions (1)
21 Jul 2017-
Abstract: We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters.

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5,200 Citations


Open accessProceedings Article
23 Feb 2016-
Abstract: Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08 percent top-5 error on the test set of the ImageNet classification (CLS) challenge

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Topics: Residual (53%)

4,015 Citations


Open accessJournal ArticleDOI: 10.1158/1055-9965.EPI-06-0034
Abstract: Mammographic features are associated with breast cancer risk, but estimates of the strength of the association vary markedly between studies, and it is uncertain whether the association is modified by other risk factors. We conducted a systematic review and meta-analysis of publications on mammographic patterns in relation to breast cancer risk. Random effects models were used to combine study-specific relative risks. Aggregate data for > 14,000 cases and 226,000 noncases from 42 studies were included. Associations were consistent in studies conducted in the general population but were highly heterogeneous in symptomatic populations. They were much stronger for percentage density than for Wolfe grade or Breast Imaging Reporting and Data System classification and were 20% to 30% stronger in studies of incident than of prevalent cancer. No differences were observed by age/menopausal status at mammography or by ethnicity. For percentage density measured using prediagnostic mammograms, combined relative risks of incident breast cancer in the general population were 1.79 (95% confidence interval, 1.48-2.16), 2.11 (1.70-2.63), 2.92 (2.49-3.42), and 4.64 (3.64-5.91) for categories 5% to 24%, 25% to 49%, 50% to 74%, and > or = 75% relative to < 5%. This association remained strong after excluding cancers diagnosed in the first-year postmammography. This review explains some of the heterogeneity in associations of breast density with breast cancer risk and shows that, in well-conducted studies, this is one of the strongest risk factors for breast cancer. It also refutes the suggestion that the association is an artifact of masking bias or that it is only present in a restricted age range.

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Topics: Breast Cancer Risk Factor (67%), Breast cancer (64%), Risk factors for breast cancer (62%) ... show more

1,687 Citations


Journal ArticleDOI: 10.1016/S1470-2045(12)70211-5
Freddie Bray1, Ahmedin Jemal2, Nathan Grey2, Jacques Ferlay1  +1 moreInstitutions (2)
01 Aug 2012-Lancet Oncology
Abstract: Summary Background Cancer is set to become a major cause of morbidity and mortality in the coming decades in every region of the world. We aimed to assess the changing patterns of cancer according to varying levels of human development. Methods We used four levels (low, medium, high, and very high) of the Human Development Index (HDI), a composite indicator of life expectancy, education, and gross domestic product per head, to highlight cancer-specific patterns in 2008 (on the basis of GLOBOCAN estimates) and trends 1988–2002 (on the basis of the series in Cancer Incidence in Five Continents), and to produce future burden scenario for 2030 according to projected demographic changes alone and trends-based changes for selected cancer sites. Findings In the highest HDI regions in 2008, cancers of the female breast, lung, colorectum, and prostate accounted for half the overall cancer burden, whereas in medium HDI regions, cancers of the oesophagus, stomach, and liver were also common, and together these seven cancers comprised 62% of the total cancer burden in medium to very high HDI areas. In low HDI regions, cervical cancer was more common than both breast cancer and liver cancer. Nine different cancers were the most commonly diagnosed in men across 184 countries, with cancers of the prostate, lung, and liver being the most common. Breast and cervical cancers were the most common in women. In medium HDI and high HDI settings, decreases in cervical and stomach cancer incidence seem to be offset by increases in the incidence of cancers of the female breast, prostate, and colorectum. If the cancer-specific and sex-specific trends estimated in this study continue, we predict an increase in the incidence of all-cancer cases from 12·7 million new cases in 2008 to 22·2 million by 2030. Interpretation Our findings suggest that rapid societal and economic transition in many countries means that any reductions in infection-related cancers are offset by an increasing number of new cases that are more associated with reproductive, dietary, and hormonal factors. Targeted interventions can lead to a decrease in the projected increases in cancer burden through effective primary prevention strategies, alongside the implementation of vaccination, early detection, and effective treatment programmes. Funding None.

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Topics: Breast cancer (56%), Cervical cancer (52%), Stomach cancer (51%)

1,554 Citations


Performance
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No. of citations received by the Paper in previous years
YearCitations
20219
20207