Neuro-fuzzy based approach for analysing 3D PET volume

Classifying tumour in positron emission tomography (PET) imaging at early stage of illness are important for radiotherapy planning, tumour diagnosis, and fast recovery. There are many techniques for analysing medical volumes, in which some of the approaches have poor accuracy and require a lot of time for processing large medical volumes. Artificial intelligence (AI) technologies can provide better accuracy and save decent amount of time. This paper proposes an adaptive neuro fuzzy inference system (ANFIS) for analysing 3D PET volumes. ANFIS performance evaluation has been carried out using confusion matrix, accuracy, and misclassification value. Two PET phantom data sets, clinical PET volume of nonsmall cell lung cancer patient, and PET volumes from seven patients with laryngeal tumours have been used in this study to evaluate the performance of the proposed approach. The proposed classification methodology of phantom and clinical oncological PET data has shown promising results and can successfully classify patient lesion.