An Accurate Ensemble Classifier for Medical Volume Analysis: Phantom and Clinical PET Study

Abstract

The predominant application of positron emission tomography (PET) in the field of oncology and radiotherapy and the significance of medical imaging research have led to an urgent need for effective approaches to PET volume analysis and the development of accurate and robust volume analysis techniques to support oncologists in their clinical practice, including diagnosis, arrangement of appropriate radiotherapy treatment, and evaluation of patients' response to therapy. This paper proposes an efficient optimized ensemble classifier to tackle the problem of analysis of squamous cell carcinoma in patient PET volumes. This optimized classifier is based on an artificial neural network (ANN), fuzzy C-means (FCM), an adaptive neuro-fuzzy inference system (ANFIS), K-means, and a self-organizing map (SOM). Four ensemble classifier machines are proposed in this study. The first three are built using a voting approach, an averaging technique, and weighted averaging, respectively. The fourth, novel ensemble classifier machine is based on the combination of a modified particle swarm optimization (PSO) approach and weighted averaging. Experimental National Electrical Manufacturers Association and International Electrotechnical Commission (NEMA IEC) body phantom and clinical PET studies of participants with laryngeal squamous cell carcinoma are used for the evaluation of the proposed approach. Superior results were achieved using the new optimized ensemble classifier when compared with the results from the investigated classifiers and the non-optimized ensemble classifiers. The proposed approach identified the region of interest class (tumor) with an average accuracy of 98.11% in clinical datasets of patients with laryngeal tumors. This system supports the expertise of clinicians in PET tumor analysis.