scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

Artificial Intelligence in Healthcare

2021 ◽  
Vol 9 (VIII) ◽  
pp. 309-311
Author(s):  
Shrey Bhagat
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Medical Specialties ◽  
Current State ◽  
Wide Range ◽  
Care Information ◽  
Structured Knowledge ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Artificial intelligence aspires to imitate the psychological functions of humans. It ushers in a perfect change in care, fueled by the rising accessibility of care information and the rapid advancement of analytics approaches. We prefer to assess the current state of AI applications in healthcare and speculate on their future. AI is being used to apply a wide range of care expertise. Machine learning algorithms for structured knowledge, such as the traditional support vector machine and neural network, and therefore the popular deep learning, as well as the tongue process for unstructured knowledge, are typical AI approaches. Cancer, neurology, medical specialties, and strokes are all major disease areas that employ AI technologies. We therefore go over AI applications in stroke in more depth, focusing on the three key areas of early detection and diagnosis, as well as outcome prediction and prognosis analysis.

scholarly journals A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Ensemble Classification ◽  
Classification Method ◽  
Ultrasound Images ◽  
Selective Ensemble ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

Project Sasha

2021 ◽  
Vol 9 (VI) ◽  
pp. 1479-1489
Author(s):  
Aparna Mete Sawant
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Detection Algorithm ◽  
Test Results ◽  
Registration Process ◽  
History Of ◽  
Haar Cascade ◽  
Detection And Diagnosis ◽  
Virtual Platform ◽  
Early Detection And Diagnosis

This paper presents an application that aids in the early detection and diagnosis of breast cancer in women, efficiently and accurately. Furthermore, the application eliminates the need for direct contact between patient and doctor by providing a virtual platform in the form of a GUI wherein the patient can upload scanned copies of test results as prescribed by an oncologist. The digitization of the registration process is done via face recognition using Haar Cascade. The application in this paper provides a platform for the doctors to- write a new prescription, view appointments, access reports, view the history of every patient; for patients to- book an appointment, view their prescriptions, access reports and review previous appointments; for pharmacists to view the prescription of a particular patient. The link between patients, doctors and pharmacists is highlighted in the proposed application. The latest object detection algorithm YOLOv3 is used for early detection of breast cancer after the image is annotated. After the training and testing, the model gives an accuracy between (75- 80)%.

Breast cancer analysis using Machine Learning algorithms

2021 ◽  
Vol 2 (1) ◽  
pp. 12-16
Author(s):  
Lina Alkhathlan ◽  
Abdul Khader Jilani Saudagar
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Causes Of Death ◽  
State Of The Art ◽  
Early Stage ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Risk Of Death ◽  
Breast Cells

Breast cancer (BC) is one of the most common types of cancer and one of the leading causes of death for women around the world. Breast cancer occurs when cells in the breast cells mutate and form a malignant tumor. State-of-the-art technologies can detect BC at an early stage, which helps in treatment and reduces the risk of death. Medical doctors commonly use breast tissue biopsy when diagnosing breast cancer, enabling them to take a microscopic look for breast tissue and determine whether the tissue is benign or malignant. To improve biopsy results, many researchers have studied the feasibility of using artificial intelligence (AI) to help doctors detect any harmful changes that may lead to cancer. In this research work detail analysis of Breast Cancer using support vector machine (SVM) and convolutional neural networks (CNN) algorithms is performed and the results show CNN has more superior results in comparison to SVM in the recognition of images affected by Breast Cancer.

scholarly journals Fuzzy relational model and genetic algorithms for early detection and diagnosis of breast cancer in Saudi Arabia

Filomat ◽  
2016 ◽  
Vol 30 (3) ◽  
pp. 547-556 ◽  
Author(s):  
Fairouz Tchier ◽  
Abir Alharbi
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithms ◽  
Saudi Arabia ◽  
Early Detection ◽  
Computing Methodology ◽  
Fitness Function ◽  
Relational Model ◽  
Breast Cancer Dataset ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast Cancer (BC) is considered as the most implacable malignancy and the leading cause of mortality among women in general and in Saudi Arabia specially. Most of the previous work in Saudi Arabia on this subject was on epidemiology, knowledge of (BC) and practice of breast self-examination (BSE), etiological factors, metastases and rate of survival. Early detection and diagnosis of Breast Cancer (BC) is an important, real-world medical problem. In this paper, we propose a soft computing methodology to build a Breast Cancer (BC) diagnosis system with high capabilities as described by Andres et al. [4] but on the Saudi Arabian breast cancer dataset and using a simplified fitness function. We focus on combining fuzzy concepts and genetic algorithms so as to automatically produce diagnostic systems to support and assist the expert to understand and evaluate its results with high classification performance.

scholarly journals Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making

2020 ◽  
Author(s):  
Mohammad Pourhomayoun ◽  
Mahdi Shakibi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mortality Rate ◽  
Mortality Risk ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Medical Decision ◽  
Support Vector ◽  
Depth Analysis

AbstractIn the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used documented data of 117,000 patients world-wide with laboratory-confirmed COVID-19. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 93% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.

scholarly journals Early Breast Cancer Prediction using Artificial Intelligence Methods

2020 ◽  
pp. 48-54
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Breast Cancer ◽  
Health Care ◽  
Early Detection ◽  
Health Care Systems ◽  
Support Vector ◽  
Kappa Score ◽  
Cancer Disease ◽  
Care Systems ◽  
Detection And Diagnosis ◽  
Automated Tool

In India, the death toll due to breast cancer is increasing at a rapid pace. Only early detection and diagnosis is the way of control but it is a major challenge in India due to lack of awareness and lethargy of Indian womentowards health care and regular check-up. But the major obstacle in India is expensive health care system and unavailability of proper infrastructure, especially in breast cancer treatment. This paper aims in obtaining an automated tool that will exploit patient’s health records and predict the tendency of being affected in breast cancer. Gradient Boost classifier is used as an automated tool that predicts the chance of being affected in breast cancer disease. Early detection of this disease will assist health care systems to provide counter measures in order to save patients’ life. The proposed model is evaluated against other peer classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN), Naïve bayes classifier, Adaboost classifier, Decision Tree (DT) classifier, and Random Forest (RF) Classifier. The proposed method achieves encouraging result with an accuracy of 97.34%, F1-Score of 0.97 Cohen-Kappa Score of 0.94 and MSE of 0.0266. The Gradient Boost algorithm attains the lowest error rate along with highest efficiency which might be the best choice of algorithm for this problem and prediction of disease.

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