Breast Cancer - Astrophysics Data System

MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer

Robust self-learning can improve image classification models' generalizability without greatly sacrificing model generalizability. We recommend the Multi-instance RST with a drop-max layer, MIRST-DM, which consists of a sequence of iteratively generated adversarial instances during training to help smoother decision boundaries on small datasets.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220501674S/abstract

Experimental and computational studies of phytomediated selenium-CuO and ZnO nanoparticles-potential drugs for breast cancer

In microbes and the MCF-7 breast cancer cell line, phytomediated selenium-copper oxide and selenium-zinc oxide nanoparticles were tested for their biological functions. Cassia angustifolia seed was used to produce selenium-supporting metal oxide nanoparticles. The minimum inhibitory concentrations for the Se-MO NPs were determined, showing that the Se-ZnO NPs have the highest antimicrobial activity. The IC 50 values were determined using different doses of Se-MO NPs on MCF-7, revealing that Se-CuO NPs exhibit elevated anticancer activity due to their low hardness and electronic characteristics as derived from their electrostatic potentials and frontier orbitals.

Source link: https://ui.adsabs.harvard.edu/abs/2022JMoSt126333113A/abstract

Breast Cancer Analysis Using Data Mining Techniques

Tumors of cells in the body lead to cancerous tumor formation. It is the additional tissue growth in the breast that starts from a single abnormal cell that leads to tumor formation. Early detection is the most cost-effective way to reduce the incidence of breast cancer deaths. A PC-based analysis system can aid in diagnosis of disease in determining the correctness of diagnostic findings. To obtain a consolidated report from the massive dataset, the MapReduce algorithm will perform mapping and reducing process. Monitoring and spreading the disease in the early stages is the first step to reducing the risk of cancer and improving the patient's healthy life.

Source link: https://ui.adsabs.harvard.edu/abs/2022ECSTr.10715895S/abstract

A Survey on Segmentation Techniques for Breast Cancer Detection

Breast cancer is the second most common health condition after lung cancer in women around the world. Early detection reduces the cancer death rate among women. This paper explores various segmentation techniques for breast cancer detection. As compared to FCM, K-means is capable of estimating tumor region boundaries.

Source link: https://ui.adsabs.harvard.edu/abs/2022ECSTr.107.6703U/abstract

Medical Image Prediction for Diagnosis of Breast Cancer Disease Comparing the Machine Learning Algorithms: SVM, KNN, Logistic Regression, Random Forest, and Decision Tree to Measure Accuracy

Machine learning using data mining techniques is increasingly used in medical research to determine disease diagnosis. The samples are divided into benign and malignant cells by SVM, KNN, Decision Tree, Random Forest, and Logistic Regression to compare the results of benign and malignant cells. Logistic Regression is shown to be more effective than the SVM, KNN, Decision Tree, and Random Forest in breast cancer detection using the Wisconsin dataset, according to the study.

Source link: https://ui.adsabs.harvard.edu/abs/2022ECSTr.10712681D/abstract

A Novel Transfer Learning Technique for Detecting Breast Cancer Mammograms Using VGG16 Bottleneck Feature

According to ICMR, 2018 in India, breast cancer accounts for the largest proportion of cancers and the second most common cancer overall that affect women with 87,090 deaths. Breast tumors are divided into two types, one benign, which is not harmful and unlikely to cause breast cancer and b malignant, and b malignant, on the other hand, the tumors are extremely painful and will form an abnormal cell that can cause cancer. VGG16 Visual Geometry Group 16 has been built with a focus on breast cancer diagnosis using mammography images obtained from the MIAS database, with a focus on breast cancer classification.

Source link: https://ui.adsabs.harvard.edu/abs/2022ECSTr.107..733P/abstract

Artificial Intelligence-Based Breast Cancer Analysis Technique

If one has a large breast cancer, the patient may be advised to do a CT scan to determine the extent of cancer in the chest cavity. One must perform CT scans of the chest and abdomen if the patient's symptoms or other findings show that cancer has extremely widespread.

Source link: https://ui.adsabs.harvard.edu/abs/2022ECSTr.107.2023R/abstract

Multislot Rectangular Microstrip Patch Antenna Design to Detect Breast Cancer

Breast cancer is the most common cancer among women. Breast cancer has risen to the top cause of death among women worldwide in recent years. X-ray mammography, magnetic resonance imaging, and ultrasound scanning can all be used to identify cancer cells in the breast. We present a new model for a multislot rectangular microstrip patch antenna with the ability to detect breast cancers in the desired FCC range in this paper. The findings indicate that the new antenna is a good candidate for breast cancer detection.

Source link: https://ui.adsabs.harvard.edu/abs/2022ECSTr.10710721A/abstract

A targeted nanoplatform co-delivery of pooled siRNA and doxorubicin for reversing of multidrug resistance in breast cancer

Multi-drug resistance has been the greatest barrier to the success of cancer patients receiving traditional chemotherapeutics or novel targeted drugs. In vitro, the targeted nanoplatform demonstrated a high suppressing effectiveness for the invasion, replication, and colony of a driamycin-resistant breast cancer cell line cells. based on biocompatible -polysine polymers, the aim of this research is to develop a new and simple approach to create a new and effective gene and drug delivery system for MDR breast cancer patients.

Source link: https://ui.adsabs.harvard.edu/abs/2022NaRes.tmp..251L/abstract

Tetrahydro-β-carboline-naphthalimide hybrids: Synthesis and anti-proliferative evaluation on estrogen-dependent and triple-negative breast cancer cells

Using MTT assay, the obtained hybrids were tested for growth inhibitory activity against MCF7 and MDA-MB-231 breast cancer cell lines. The presented paper shows that the resulting template demonstrated a high growth inhibitory activity on Breast Cancer cells with a high safety profile, and that, in turn, the new template displayed a good success rate of hybridization method in clubbing two individual anti-cancer cores i. e.

Source link: https://ui.adsabs.harvard.edu/abs/2022JMoSt126233053S/abstract

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