Prostate cancer (CaP) is the most frequently diagnosed malignancy after skin cancer and the second leading cause of cancer-related to male deaths after lung cancer. In the UAE, a recent study by the Health Authority of Abu Dhabi revealed that CaP is the third most common cancer in the UAE, affecting around 313 males out of every 100,000 of the population, making it the fourth most prevalent form of disease in among men in the UAE. Early and accurate detection will allow clinicians to initiate early intervention and appropriate treatment at earlier stages. Thus, it can potentially decrease the mortality rate of CaP. In addition, it is estimated that early detection of CaP using invasive MRI-based systems will reduce the diagnostic costs by 90%, benefiting patients, payers, and, in a bundled payment system, providers as well. This research will also bring more awareness about the importance of early prostate cancer screening.
Most of the MRI-based computer-aided diagnosis systems proposed so far, indicate the presence of CaP either on whole MRI scan or in a sectional part of it without providing any information about the location of the CaP lesion in the MRI scan image. In this project, we propose to fill this gap by designing an advanced machine learning system capable of accurately locating the related lesions in the MRI scans. This information will provide pertinent guidance for the physician when performing the biopsy, thus reducing the risk of misdiagnosis.