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Author: Akor Ugwu Publisher: GRIN Verlag ISBN: 334640482X Category : Medical Languages : en Pages : 77
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: Akor Ugwu Publisher: GRIN Verlag ISBN: 334640482X Category : Medical Languages : en Pages : 77
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: Pratheep Kumar Publisher: Infinite Study ISBN: Category : Mathematics Languages : en Pages : 11
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
Author: Janmenjoy Nayak Publisher: Springer Nature ISBN: 3030719758 Category : Technology & Engineering Languages : en Pages : 461
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: H.D.Cheng Publisher: Infinite Study ISBN: Category : Languages : en Pages : 19
Book Description
Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast.
Author: Priyanka Vaidya Publisher: ISBN: Category : Bioinformatics Languages : en Pages : 107
Book Description
"Breast cancer is the second most common form of cancer amongst females and also the fifth most cause of cancer deaths worthwide. In case of this particular type of malignancy, early detection is the best form of cure and hence timely and accurate diagnosis of the tumor is extremely vital. Extensive research has been carried out on automating the critical diagnosis procedure as various machine learning algorithms and software tools have been deployed to aid physicians in optimizing the decision task effectively. In this research, we present a novel matrix of an artificial neural network system to effectively classify breast cancer tumors as either malignant or benign. This classification system makes use of both clinical as well as genetic data. Artificial neural networks of different architectures are incorporated in the system to classify both the image based clinical dataset as well as the microarray dataset derived from blood cells. Both the datasets are subjected to indificual analysis to compute the optimum number of input features to the neural network matrix. Randomly selected sample instances from both the clinical and microarray original datasets then serve as an input to the Dempster-Shafer theory of evidence block where the outputs are fused to provide a final diagnostic assessment and compared with the neural network analysis. The results indicate that the fused output of the Dempster-Shafer block significantly outperform the individual classifier's outputs."--Abstract.
Author: Janmenjoy Nayak Publisher: Springer ISBN: 9811305145 Category : Technology & Engineering Languages : en Pages : 848
Book Description
The volume contains original research findings, exchange of ideas and dissemination of innovative, practical development experiences in different fields of soft and advance computing. It provides insights into the International Conference on Soft Computing in Data Analytics (SCDA). It also concentrates on both theory and practices from around the world in all the areas of related disciplines of soft computing. The book provides rapid dissemination of important results in soft computing technologies, a fusion of research in fuzzy logic, evolutionary computations, neural science and neural network systems and chaos theory and chaotic systems, swarm based algorithms, etc. The book aims to cater the postgraduate students and researchers working in the discipline of computer science and engineering along with other engineering branches.
Author: Khalid Shaikh Publisher: Springer Nature ISBN: 3030592081 Category : Technology & Engineering Languages : en Pages : 107
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: Meenu Gupta Publisher: CRC Press ISBN: 1000508668 Category : Computers Languages : en Pages : 202
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: Sanjana Balasubramanian Publisher: ISBN: Category : Languages : en Pages :
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: Akor Ugwu
Author: Akor Ugwu
Author: Pratheep Kumar
Author: Janmenjoy Nayak
Author: H.D.Cheng
Author: Johan Ruuskanen
Author: Priyanka Vaidya
Author: Janmenjoy Nayak
Author: Khalid Shaikh
Author: Meenu Gupta
Author: Sanjana Balasubramanian