Understanding a Cancer Diagnosis

Wellington Pinheiro dos Santos, Maira Araujo de Santana, and Washington Wagner Azevedo da Silva (Editors)
Department of Biomedical Engineering, Federal University of Pernambuco, Recife, Brazil

Series: Cancer Etiology, Diagnosis and Treatments
BISAC: MED062000

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Volume 10

Issue 1

Volume 2

Volume 3

Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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A diagnosis can be a deep investigative process, complex by nature. The diagnostic processes have become much more multidisciplinary, demanding the use of an eclectic set of technological methodologies and tools, especially from the Fourth Revolution. Biosensors, Artificial Intelligence, Internet of Things and 3D Printing have become common terms in health research. Cancer in all its forms has become one of the biggest public health issues of the twentieth century. Among all types of cancer, breast cancer is the most dangerous for older and middle-aged women; it is also the most common form of cancer among the female population. Breast cancer is among the five most common cancers worldwide. This disease has been proliferating in developed, underdeveloped and developing countries. Its incidence rate is increasing with the average life expectancy of the population and with the adoption of new forms of consumption.

There are some preventive strategies for breast cancer, such as stimulating visual inspection and touching of the breasts. However, they are not efficient enough to impact breast cancer mortality rate because the disease is still being diagnosed late in many cases. Therefore, a deeper understanding of the disease is necessary, including its risk factors and strategies for early identification and efficient treatment. The existence of these tools in public healthcare systems is important because they may contribute to increasing the chances of cure and the treatment options, decreasing mortality rates. Herein this collection book, we present to readers a set of works from the state-of-the-art dealing with cancer diagnosis using biosensors, artificial intelligence and other approaches. We hope this collection could present some of the state of the art of innovative techniques based on the Fourth Industrial Revolution to support early and accurate diagnosis of cancer, especially breast cancer.
(Imprint: Nova Medicine and Health)

Preface

Acknowledgements

Chapter 1. Considerations of Novel Diagnostic and Therapeutic Approaches to Metastatic Triple-Negative Breast Cancer
(Katarzyna Rygiel, Department of Family Practice, Medical University of Silesia, Zabrze, Poland)

Chapter 2. Morphological Decomposition to Detect and Classify Lesions in Mammograms
(Sidney Marlon Lopes de Lima, Abel Guilhermino da Silva Filho and Wellington Pinheiro dos Santos, Center of Informatics, Federal University of Pernambuco, Recife, Brazil, and others)

Chapter 3. Breast Lesions Classification in Frontal Thermographic Images Using Intelligent Systems and Moments of Haralick and Zernike
(Maíra Araújo de Santana, Jessiane Mônica Silva Pereira, Rita de Cássia Fernandes de Lima and Wellington Pinheiro dos Santos, Department of Biomedical Engineering, Federal University of Pernambuco, Recife, Brazil, and others)

Chapter 4. Lesion Detection in Breast Thermography Using Machine Learning Algorithms without Previous Segmentation
(Jessiane Mônica Silva Pereira, Maíra Araújo de Santana, Rita de Cássia Fernandes de Lima and Wellington Pinheiro dos Santos, Polytechnique School of the University of Pernambuco, Recife, Brazil, and others)

Chapter 5. Dialectical Optimization Method as a Feature Selection Tool for Breast Cancer Diagnosis Using Thermographic Images
(Jessiane Mônica Silva Pereira, Maíra Araújo de Santana, Washington Wagner Azevedo da Silva, Rita de Cássia Fernandes de Lima, Sidney Marlon Lopes de Lima and Wellington Pinheiro dos Santos, Polytechnique School of the University of Pernambuco, Recife, Brazil, and others)

Chapter 6. Method for Classification of Breast Lesions in Thermographic Images Using ELM Classifiers
(Jessiane Mônica Silva Pereira, Maíra Araújo de Santana, Rita de Cássia Fernandes de Lima, Sidney Marlon Lopes de Lima and Wellington Pinheiro dos Santos, Polytechnique School of the University of Pernambuco, Recife, Brazil, and others)

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