Breast cancer detection by leveraging Machine Learning

Vaka, Anji Reddy; Soni, Badal; Reddy, K. Sudheer

doi:10.1016/j.icte.2020.04.009

Cited by 169 publications

(54 citation statements)

References 3 publications

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“…ML has been employed for improving BrCa diagnosis [ 15 , 16 ] and prognosis [ 9 , 17 ] in different types of datasets; however, to the best of our knowledge, only List et al [ 18 ] has applied ML to BrCa methylation data, although still failing to construct an accurate model to classify disease subtypes. In our opinion, there is still much to gain by exploiting ML approaches in analyzing genome-wide methylation BrCa datasets, both for knowledge mining as well as to construct clinically relevant models.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Παναγοπούλου

Karaglani

Manolopoulos

et al. 2021

Cancers

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DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982–1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921–1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920–1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

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Section: Introductionmentioning

confidence: 99%

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Παναγοπούλου

Karaglani

Manolopoulos

et al. 2021

Cancers

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“…Many examples of ML and AI medical tools for diagnosis of non-infectious (diabetic, cancer, Parkinson's, heart diseases etc.) [25][26][27][28][29] and contagious diseases (HIV, Ebola, SARS, and COVID-19) [30][31][32][33] were developed. In a recent series, ML methods have been successfully used for Ebola outbreak estimation.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

Rasheed

Jamıl

Hameed

et al. 2021

Interdiscip Sci Comput Life Sci

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Graphic abstract The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

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“…In recent years, machine learning has been successfully applied to image recognition, object recognition, and text classification. With the advancement of computer-aided diagnosis technology, machine learning has also been successfully applied to breast cancer diagnosis (2)(3)(4)(5)(6)(7)(8). There are two common methods, histopathological images classification based on artificial feature extraction and traditional machine learning methods, and histopathological images classification based on deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

Qiao

Zhang

et al. 2021

Front. Oncol.

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Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

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Breast cancer detection by leveraging Machine Learning

Cited by 169 publications

References 3 publications

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

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