Engineering - Deep Learning in Cancer Diagnostics: A Feature-based Transfer Learning Evaluation (SpringerBriefs in Applied Sciences and Technology)

Deep Learning in Cancer Diagnostics: A Feature-based Transfer Learning Evaluation (SpringerBriefs in Applied Sciences and Technology)

Name: Deep Learning in Cancer Diagnostics: A Feature-based Transfer Learning Evaluation (SpringerBriefs in Applied Sciences and Technology)
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Book Synopsis: Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral, and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer. Read more

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Discover the groundbreaking research on Deep Learning in Cancer Diagnostics with our feature-based Transfer Learning Evaluation book. With cancer being the leading cause of mortality globally, it's essential to have accurate diagnostic tools. This book showcases the latest computational methods used for diagnosing common cancers like breast, lung, oral, and skin. Stay ahead in the field of cancer diagnostics with this essential resource.

Get a sneak peek into the future of cancer diagnosis with the SpringerBriefs in Applied Sciences and Technology book on Deep Learning in Cancer Diagnostics. As per the World Health Organization, cancer contributes significantly to mortality rates worldwide, making accurate diagnostics crucial. From misdiagnosis to advanced technology solutions, this book is a must-have for medical professionals and researchers seeking key insights and strategies to combat cancer effectively.

Empower yourself with the knowledge and expertise in cancer diagnostics through innovative computational methods presented in this comprehensive book. Take a closer look at the feature-based transfer learning evaluation for breast, lung, oral, and skin cancers. With state-of-the-art models and insightful findings, this book offers a valuable resource for stakeholders involved in cancer diagnostics. Stay informed, stay proactive, and make a difference in the fight against cancer.

Ready to revolutionize your approach to cancer diagnostics? Dive into the detailed evaluation of common cancers in the Deep Learning in Cancer Diagnostics book. Benefit from cutting-edge computational methods and transfer learning models to enhance the accuracy of cancer diagnosis. Equip yourself with the latest insights and advancements in cancer diagnostics to make informed decisions and drive better patient outcomes.

Explore the Deep Learning in Cancer Diagnostics book now.

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