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AI system for oncological volumetric medical PET classification

Positron emission tomography (PET) imaging is an emerging medical imaging modality. Due to its high sensitivity and ability to model physiological function, it is effective in identifying active regions that may be associated with different types of tumour. Increasing numbers of patient scans have led to an urgent need for the development of a new efficient data analysis system to aid clinicians in the diagnosis of disease and save a decent amount of the processing time, as well as the automatic detection of small lesions.

In this research, an automated intelligent system for oncological PET volume analysis has been developed. Experimental NEMA (national electrical manufacturers association) IEC (International Electrotechnical Commission) body phantom data set, Zubal anthropomorphic phantom data set with simulated tumours, clinical data set from a patient with histologically proven non-small cell lung cancer, and clinical data sets from seven patients with laryngeal squamous cell carcinoma have been utilised in this research. The initial stage of the developed system involves different thresholding approaches and transforming the processed volumes into the wavelet domain at different levels of decomposition by deploying HAAR wavelet transform. K-means approach is also deployed to classify the processed volume into a distinct number of classes. The optimal number of classes for each processed data set has been obtained automatically based on the Bayesian information criterion.

The second stage of the system involves artificial intelligence approaches including feedforward neural network, adaptive neuro-fuzzy inference system, self-organising map, and fuzzy C-means. The best neural network design for PET application has been thoroughly investigated. All the proposed classifiers have been evaluated and tested on the experimental, simulated and clinical data sets. The final stage of the developed system includes the development of a new optimised committee machine for PET application and tumour classification.

Meet the Principal Investigator(s) for the project

Dr Maysam Abbod
Dr Maysam Abbod - Education Dr Maysam F. Abbod (FIET, CEng, SMIEEE, SFHEA) He received BSc degree in Electrical Engineering fromUniversity of Technology in 1987. PhD in Control Engineering fromUniversity ofSheffield in 1992. From 1993 to 2006 he was with the Department of Automatic Control and Systems Engineering at theUniversity of Sheffield as a research associate and senior research fellow

Related Research Group(s)


AI Social and Digital Innovation - Social, economic and strategic effects of AI and associated technologies. Impact of AI and related technologies on societies, organisations and individuals.

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Project last modified 21/11/2023