Classification of Prognosis in Breast Cancer Patients from AMCL Analysis Using Machine Learning Techniques PDF Download
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Author: Johan Ruuskanen
Publisher:
ISBN:
Category :
Languages : en
Pages : 81
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Book Description
Author: Johan Ruuskanen
Publisher:
ISBN:
Category :
Languages : en
Pages : 81
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Author: Akor Ugwu
Publisher: GRIN Verlag
ISBN: 334640482X
Category : Medical
Languages : en
Pages : 77
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Book Description
Diploma Thesis from the year 2020 in the subject Medicine - Diagnostics, grade: 3.55, , course: Computer Science, language: English, abstract: The study will classify breast cancers into foremost problems: (Benign tumor and Malignant tumor). A benign tumor is a most cancers does now not invade its surrounding tissue or spread around the host. A malignant tumor is another kind of cancers which can invade its surrounding tissue or spread around the frame of the host. Benign cancers on uncommon event can also surely result in someone’s death, but as a fashionable rule they're no longer nearly as horrific because the malignant cancers. The malignant cancers at the contrary are like those killer bees. In this situation, you do not need to be doing something to them or maybe be everywhere near their hive, they will just spread out and attack you emass – they could even kill the individual if they are extreme enough. Manual manner of cancer category into benign and malignant may be very tedious, susceptible to human error and unnecessarily time consuming. The proposed system while constructed can robotically classify the sort of most cancers into the safe (benign) and also the risky (malignant). This machine plays this role through the usage of machine getting to know algorithm. The following is the extensive of this new system: Classification mistakes could be notably removed, early analysis of disorder, removal of possible human mistakes and the device does no longer die. However, the researcher seeks to detect and assess the class of breast using Machine learning.
Author: Janmenjoy Nayak
Publisher: Springer Nature
ISBN: 3030719758
Category : Technology & Engineering
Languages : en
Pages : 461
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Book Description
This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.
Author: Sanjana Balasubramanian
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Today there are more than 1.15 million cases of breast cancer diagnosed worldwide annually. At present, only small numbers of accurate prognostic and predictive factors are used clinically for managing the patients with breast cancer. Early detection of this fatal disease is very important which helps in decreasing the morality rate and increasing the survival period of breast cancer patients. The project uses Mammography which is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. To detect breast cancer in mammogram, image segmentation is performed with the help of Fuzzy C-means (FCM) technique. Further those segmented regions features are extracted, and it is trained completely, finally trained images are classified by the efficient classifier of different classes in mammogram. Texture features are extracted using a feature extraction technique like Multi-level Discrete Wavelet Transform, Principal Component Analysis (PCA), Gray-level Co-occurrence Matrix (GLCM). Morphological operators are used to distinguish masses and micro calcifications from the background tissue and KNN algorithm is used for classification. The boundaries of tumor affected region in mammogram are marked and displayed to the doctor, along with area of tumor.
Author: Khalid Shaikh
Publisher: Springer Nature
ISBN: 3030592081
Category : Technology & Engineering
Languages : en
Pages : 107
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Book Description
This book provides an introduction to next generation smart screening technology for medical image analysis that combines artificial intelligence (AI) techniques with digital screening to develop innovative methods for detecting breast cancer. The authors begin with a discussion of breast cancer, its characteristics and symptoms, and the importance of early screening.They then provide insight on the role of artificial intelligence in global healthcare, screening methods for breast cancer using mammogram, ultrasound, and thermogram images, and the potential benefits of using AI-based systems for clinical screening to more accurately detect, diagnose, and treat breast cancer. Discusses various existing screening methods for breast cancer Presents deep information on artificial intelligence-based screening methods Discusses cancer treatment based on geographical differences and cultural characteristics
Author: Johnny Chan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Breast cancer is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. Thus, the correct diagnosis of breast cancer and classification of patients into malignant or benign groups is the subject of much research. The objective of this paper is to review machine learning techniques and their applications in breast cancer diagnosis and prognosis. Because of its unique advantages in critical features detection from complex breast cancer datasets, machine learning is widely recognised as the methodology of choice in breast cancer pattern classification and forecast modelling. This paper provides an overview of machine learning techniques including artificial neural networks, support vector machines, decision trees, and k-nearest neighbours. Then, it investigates their applications in breast cancer. The primary data is drawn from the Wisconsin breast cancer database which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model is also shown.
Author: Meenu Gupta
Publisher: CRC Press
ISBN: 1000508668
Category : Computers
Languages : en
Pages : 202
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Book Description
Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.
Author: Lakhmi C Jain
Publisher: World Scientific
ISBN: 9814492671
Category : Computers
Languages : en
Pages : 350
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Book Description
The main aim of this book is to present a sample of recent research on the application of novel artificial intelligence paradigms to the diagnosis and prognosis of breast cancer. These paradigms include neural networks, fuzzy logic and evolutionary computing. Artificial intelligence techniques offer advantages — such as adaptation, fault tolerance, learning and human-like behavior — over conventional computing techniques. The idea is to combine the pathological, intelligent and statistical approaches to enable simple and accurate diagnosis and prognosis.This book is the first of its kind on the topic of artificial intelligence in breast cancer. It presents the applications of artificial intelligence in breast cancer diagnosis and prognosis, and includes state-of-the-art concepts in the field. It contains contributions from Australia, Germany, Italy, UK and the USA.
Author: Suleyman Vural
Publisher:
ISBN:
Category :
Languages : en
Pages : 100
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Book Description
The high degree of heterogeneity observed in breast cancers makes it very difficult to classify cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. In this study, we explore the use of gene mutation profiles to classify, characterize and predict the subgroups of breast cancers. We analyzed the whole exome sequencing data from 358 ethnically similar breast cancer patients in The Cancer Genome Atlas (TCGA) project. Identified somatic and non-synonymous single nucleotide variants were assigned a quantitative score (C-score) that represents the extent of negative impact on the function of the gene. Using these scores with a non-negative matrix factorization method, we clustered the patients into three subgroups. By comparing the clinical stage of patients among the three subgroups, we identified an early-stage-enriched and a late-stage-enriched subgroup. Comparison of the C-scores (mutation scores) of these subgroups identified 358 genes that carry significantly higher rates of mutations in the late-stage-enriched subgroup. Functional characterization of these genes revealed important functional gene families that carry a heavy mutational load in the late-state-enriched subgroup. Finally, using the identified subgroups, we also developed a supervised classification model to predict the likely stage of patients, given their mutation profiles, hence provide clinical insights to help devise an effective treatment plan. This study demonstrates that gene mutation profiles can be effectively used with machine-learning methods to identify clinically distinguishable subgroups of cancer patients. Genes and gene families that carry a heavy mutational load in late-stage-enriched cancer patients compared to early-stage-enriched subgroup were also identified from functional analysis of genes. The classification model developed in this method could provide a reasonable prediction of the stage of cancer patients solely based on their mutation profiles. This study represents the first use of only somatic mutation profile data to identify and predict breast cancer subgroups and this generic methodology could also be applied to other cancer datasets.
Author: Pratheep Kumar
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 11
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Book Description
Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier
