PET Volume Analysis Based on Committee Machine for Tumour Detection and Quantification

The prevailing application of positron emission tomography (PET) in clinical oncology and the increasing number of patient scans have led to a real need for efficient PET volume handling and the development of new volume analysis and classification approaches to aid clinicians in the diagnosis of diseases, planning of treatment, and patient fast recovery. Analysing large medical volumes using traditional techniques produces sometimes poor accuracy. Thus, this paper proposes a committee machine based on feed forward neural network, neuro-fuzzy, self-organising map, fuzzy c-means, and K-means. Different combination approaches were evaluated and the best results were achieved using weighted averaging approach. PET Zubal phantom data set containing 3 lung tumours has been utilised to validate the proposed committee machine which has shown promising results.