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This book is aimed to cover the recent investigations of spectral analysis for biological applications especially dealing with cancer detection, including microparticles detection in blood and urine, etc. It is known that there is no permanent cure for metastatic cancers, nevertheless, detecting it at an early stage and treating it accordingly, is one way to reduce its severity. i.e., increasing survival rate. To effectively detect tumours at an early stage, several optical techniques such as Mammography, Ultrasound, Computed Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance are widely preferred.
This book is dedicated to discussing the technological advancement, challenges, and the future perspective of some of these spectral analysis techniques for cancer diagnosis. The authors believe this book will provide the fundamental knowledge, awareness and modes of treatments that are available to readers.
Key Features:
- This book will provide a comprehensive overview for spectral analysis for cancer diagnostics
- Includes extensive analysis of early cancer detection techniques
- Covers modern techniques of saliva-based cancer detection techniques
- Covers an investigation of Deep Learning techniques for cancer diagnostics
- Includes extensive references to enhance further study and implementation
- Spectral analysis for biomedical applications: An Overview Bryan Hennelly, Inbarasan Muniraj et. al
- Hyperspectral microscopic imaging for saliva-based breast cancer detection. Inbarasan Muniraj et al.
- Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging in cancer. Baowei Fei, Univ of Texas Southwestern Medical Center. (USA)
- Polarimetric imaging for the detection of synthetic models of viral particles Emilio Gomez Gonzalez, Universidad de Sevilla (Spain)
- Biomedical hyperspectral imaging using broadband coherent anti-Stokes Raman spectroscopy Rangaraj M. Rangayyan, University of Calgary (Canada)
- FTIR/Photothermal hyperspectral imaging for cancer biomedical applications Giovanni Sparacino, University of Padova (Italy)
- Stimulated Raman spectroscopy for virtual histopathological staining. Tanveer Talukdar, University of Illinois at Urbana-Champaign (USA)
- Raman/NIR hyperspectral imaging for cancer tissue analysis Priyanka Sharma, Indian Institute of Technology, New Delhi (India)
- Hyperspectral imaging for intraoperative diagnosis of colon cancer metastasis in a liver Martin Stridh Lund University (Sweden)
- Digital pathology with hyperspectral imaging for colon and ovarian cancer Yaniv Zigel, Ben-Gurion University of the Negev (Israel)