Advances in Brain Tumors

A brain tumor occurs when abnormal cells form within the brain. There are two main types of tumors: malignant (cancerous) tumors and benign (non-cancerous) tumors. These can be further classified as primary tumors, which start within the brain, and secondary tumors, which most commonly have spread from tumors located outside the brain, known as brain metastasis tumors. All types of brain tumors may produce symptoms that vary depending on the size of the tumor and the part of the brain that is involved. Where symptoms exist, they may include headaches, seizures, problems with vision, vomiting and mental changes.Other symptoms may include difficulty walking, speaking, with sensations, or unconsciousness.

In the present book, ten typical literatures about Brain tumors published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on Brain tumors. We hope this book can demonstrate advances in Brain tumors as well as give references to the researchers, students and other related people.

Sample Chapter(s)
Preface (265 KB)
Components of the Book:
  • Chapter 1
    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification
  • Chapter 2
    BT-Net: An end-to-end multi-task architecture for brain tumor classification, segmentation, and localization from MRI images
  • Chapter 3
    OpenPBTA: The Open Pediatric Brain Tumor Atlas
  • Chapter 4
    Brain tumors recognition based on deep learning
  • Chapter 5
    Diagnosing the MRI Brain Tumour Images through RNN-LSTM
  • Chapter 6
    A validated LC-MS/MS method for determination of neuro-pharmacokinetic behavior of niraparib in brain tumor patients
  • Chapter 7
    Incidence of alopecia in brain tumour patients treated with pencil scanning proton therapy and validation of existing NTCP models
  • Chapter 8
    Brain tumor recognition by an optimized deep network utilizing ammended grasshopper optimization
  • Chapter 9
    GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI
  • Chapter 10
    RanMerFormer: Randomized vision transformer with token merging for brain tumor classification
Readership: Students, academics, teachers and other people attending or interested in brain tumors.
Joshua A. Shapiro
Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Bala Cynwyd, PA 19004, USA

Krutika S. Gaonkar
Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA

William Knight
Ivy Brain Tumor Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA

and more...
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