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A Review of Breast Cancer Detection Using Deep Learning Techniques
Published in Archana Mire, Vinayak Elangovan, Shailaja Patil, Advances in Deep Learning for Medical Image Analysis, 2022
Abhishek Das, Mihir Narayan Mohanty
A concatenated model containing three machine learning techniques has been proposed for breast cancer classification [20]. The important parameters of the data were identified and data size was reduced using principal component analysis. The resized data were then passed through a multiple-layer perceptron (MLP) model for feature generation. These features were then used for training and classification by the SVM model. The model was verified using the Manuel Gomes dataset. This dataset contains ten parameters: age of the participants, body mass index, glucose, insulin, adiponectin, leptin, chemokine monocyte chemoattractant protein, homeostasis model assessment, resistin, and the corresponding labels representing whether the patient has cancer or is healthy. The model provided 86.97% classification accuracy; this figure needs to be improved to compete with deep learning-based results.
Case Studies/Success Stories on Machine Learning and Data Mining for Cancer Prediction
Published in Meenu Gupta, Rachna Jain, Arun Solanki, Fadi Al-Turjman, Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective, 2021
Chander Prabha, Geetika Sharma
Breast cancer is the biggest health problem faced by women all over the world. One of the major causes of breast cancer is being overweight [32]. In order to diagnose these problems and make predictions, many classifications using ML have been employed. In breast cancer detection, basically women are divided on the basis of their BMI and also on whether they are affected by breast cancer or not i.e. (1) No cancer with BMI less than 25 kg/m2. (2) No cancer with BMI greater than 25 kg/m2. (3) Breast cancer with BMI less than 25 kg/m2. (4) Breast cancer with BMI greater than 25 kg/m2. Besides BMI, two other factors are also used, i.e, glucose metabolism and insulin resistance [33]. The detection process is done by using any ML algorithm in which we first collect data on age, BMI, glucose, lepton, insulin, Resistin, Adiponectin, etc., and then pre-process this data. For implementing the ML algorithm, two libraries were used. One was ML lib, which provides many ML algorithms for cluster reduction, regression, dimensionality reduction, classification, and also for model evaluation, and the other library was ML package for ML routines, which provides an API to perform cross validation [34]. Mainly, K-nearest neighbor, decision tree, classification and regression tree (CART), Naive Bayes, and SVM were used for breast cancer detection at an early stage.
Effects of Whole Body Vibration in Adult Individuals with Metabolic Syndrome
Published in Redha Taiar, Christiano Bittencourt Machado, Xavier Chiementin, Mario Bernardo-Filho, Whole Body Vibrations, 2019
D. da Cunha de Sá-Caputo, M. Fritsch Neves, Mario Bernardo-Filho
Oda (2012) pointed out that high energy fast-food environment, sedentary life style, and other obesogenic socioeconomic environment factors have contributed to an obesity pandemic in the developed world. Moreover, genetic predisposition to obesity and proinflammatory reactions would be also associated with the MetS. As the adipose tissue secretes humoral substances, such as tumor necrosis factor-a (TNF-a), leptin, adiponectin, resistin, visfatin, monocyte chemoattractant protein-1, retinol binding protein-4, and adipocyte-type fatty acid binding protein, obesity has been considered as an endocrine and inflammatory disorder related with IR rather than anthropometric fatness.
Hydroxychloroquine improves high-fat-diet-induced obesity and organ dysfunction via modulation of lipid level, oxidative stress, and inflammation
Published in Egyptian Journal of Basic and Applied Sciences, 2023
Mohamed A Hasan, Omar A. Ammar, Maher A Amer, Azza I Othman, Fawzia Zigheber, Mohamed A El-Missiry
Obesity is associated with inflammation, and it is characterized by disturbance in the level of pro-inflammatory cytokines, including tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) [5]. Moreover, adipokines such as leptin, adiponectin, resistin, and visfatin, which are secreted from adipose tissue, show disrupted levels in obesity [6]. Leptin synthesized and secreted by white adipose tissue regulates the appetite through leptin receptors primarily in the hypothalamic nuclei, and it is expressed in several organs and immune cells [7]. Adiponectin is a cytokine that is released from adipose tissues; thus, it can be grouped in adipokines. It plays a number of molecular and cellular roles in physiological processes, primarily lipid metabolism, immune response, inflammation, and insulin sensitivity [8]. Deregulation of these cytokines is implicated in obesity and diabetes-related inflammatory response [9]. Leptin and adiponectin secretions are regulated in relation to the degree of adiposity. Plasma leptin concentration is elevated in individuals with obesity in proportion to the body mass index, whereas adiponectin secretion decreases in relation to the amount of adipose tissue [10].
Associations between changes of smartphone pedometer-assessed step counts and levels of obesity-related breast cancer biomarkers in non-cancer women: A population-based observational study
Published in Journal of Sports Sciences, 2023
Xunying Zhao, Xiaohua Liu, Xueyao Wu, Ping Fu, Xiaofan Zhang, Min Zhou, Yu Hao, Bin Xu, Lanping Yan, Jinyu Xiao, Xingyue Li, Liang Lv, Huifang Yang, Zhenmi Liu, Chunxia Yang, Xin Wang, Jiaqiang Liao, Xia Jiang, Ben Zhang, Jiayuan Li
Obesity, a well-established public health challenge worldwide, has been confirmed as one of the risk factors for female breast cancer (BC) (Picon-Ruiz et al., 2017). As demonstrated by a prospective study of 1.2 million women who were followed up for a mean of 5.4 years, obese women have a nearly 30% increased risk of postmenopausal BC compared to normal-weight women (Reeves et al., 2007). For premenopausal BC, despite the inconsistent associations observed in different ethnic groups, two meta-analyses among Asian women reported a consistently increased risk due to obesity (Amadou et al., 2013; Renehan et al., 2008). Several pathways are thought to underlie the obesity-BC relationship, including sub-clinical chronic inflammation, alterations in the adipocytokine pathophysiology, and insulin resistance and abnormalities in the insulin-like growth factor-1 (Avgerinos et al., 2019). Through alterations in protein biomarkers, obesity can affect cancer cell proliferation and micro-environment, fostering BC development (Khan et al., 2013; Wu et al., 2021). Indeed, in our previous overview, we identified six obesity-related proteins (out of > 20 metabolites) that significantly affect BC risk based on evidence aggregated from meta-analyses (Wu et al., 2021). Five proteins were further replicated as BC biomarkers through a case-control study with 539 Chinese women (Diao et al., 2021) namely C-reactive protein (CRP) (Chan et al., 2015) insulin-like growth factor binding proteins (IGFBP-3) (McCaig et al., 2002) adiponectin (ADP) (Gu et al., 2018) resistin (RETN) (Patrício et al., 2018) and soluble leptin receptor (sOB-R) (Rodrigo et al., 2017).