Automated Brain Tumour Classification

Computer Assisted Analysis and Diagnosis is one of rising areas of research in the Medical Domain. Many techniques have been developed to enhance diagnosis throughput and reduce inter-rater variability. However, the domain of medical image analysis is complex, requires high accuracies and real-time analysis. In this book, we present a technique for classification of brain tumor subtypes using image processing and machine learning techniques. Image analysis forms an important aspect of any diagnosis routine. Using automated techniques a medical professional may be assisted to acquire a more objective and robust diagnosis. The book describes a state-of-the-art technique for medical image analysis and shows how machine intelligence may be used to acquire a diagnosis. The work shows that automated diagnosis is possible as high accuracies are obtained. This work will be useful for anyone interested in carrying out further research in the area or finds this area of work interesting.

Autorentext

Dr. Hammad Qureshi, PhD Computer Science from University of Warwick, UK. Area of interest includes Wavelet Transforms, Medical Image Processing and Machine Learning. Assistant Professor at National University of Science and Technology, Pakistan.