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.
Highlights
The investigation and analysis of the volume of positron emission tomography (PET) is crucial for different clinical and diagnosis procedures, such as decreasing noise, artifact evacuation, tumor evaluation in the management stage, and to help plan the appropriate radiotherapy treatment for patients [1]
The second sub-section discusses the results from the optimized committee machine, CM4, with a focus on the accuracy of region of interest class
Class 4, which represents the simulated tumor, had 10 voxels misclassified in class 1, and the other 42 voxels were misclassified in class 3
Summary
The investigation and analysis of the volume of positron emission tomography (PET) is crucial for different clinical and diagnosis procedures, such as decreasing noise, artifact evacuation, tumor evaluation in the management stage, and to help plan the appropriate radiotherapy treatment for patients [1]. Using fluorodeoxyglucose (FDG)-PET have shown its advantage in the analysis, organization and assessment of patient reactions to treatment [2]–[4]. In spite of the fact that therapeutic volume examination seems basic, in-depth knowledge of the organs and physiology is necessary to achieve such analysis from clinical restorative images. The clinical expert monitors each slice, delineates the borders from among the images, and characterizes every area.
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