Abnormality Classification in PET Images of Prostate Tumour using Neural Network Methods
Keywords:
PET, Prostrate, cancer, medical image, machine learning, convolutional neural networksAbstract
The second most commonly affecting disease among males across the world is prostate cancer. The occurrence of prostate cancer has shown significant variation across the world. Many countries have done extensive study on the distribution of the disease and its characteristics to find the vital parameters and methods that could enable timely and precise diagnosis and therapy.Thisincludes the analysis of information obtained from different methods in decision making. Most commonly blood tests to find PSA levels is the primary test which is generally followed by MRI guided biopsies. In this study, application of computer aided diagnostic method is implemented and the outcomes are analysed with the intention that the use of AI would reduce the subjectivity of the findings at large. Use of Artificial Intelligence in cancer diagnosis help for early diagnosis, treatment and monitoring enabling remote access to experts. Functional imaging techniques when combined with CT or MRI scans should facilitate quick diagnosis times to allow for rapid response and treatment. However, positron emission tomography, when used for prostate cancer imaging, still relies on manual contour drawing and feature extraction to pinpoint tumour location. This process can take up to several days.
In this paper, an attempt has been made to streamlinethe process by performing automated tumour location pinpointing using image processing techniques. We have also used machine learning techniques and compared the results to automatically diagnose the tumour as benign or malignant. The back propagation method, Support Vector Machine approach and Convoluted Neural network models have been performed.
The automated process with specialist oversight may allow and enable faster response time and more effective treatment enabling computer assisted diagnostic approach facilitating both the qualitative and quantitative aspects.
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