Automated Detection Of Prostate Cancer from Multiparametric MRI Using Deep Convolutional Neural Networks
Abstract
One of the most prevalent cancers and the second largest cause of cancer-related
deaths in males is prostate cancer (PCa).Prostate cancer age-standardized incidence
rates have grown in Uganda, rising from 41.6 to 60.5 per 100,000 men. Compared
to 98% of African American patients. In Africa only 46.9%[1] of patients diagnosed
can live up to 5 years after diagnosis compared to 98% [2] of African American
patients. This is caused by late diagnosis of victims who are found at later stages
with incurable tumours.PCa can be treated if detected at its earlier stages.[3].
When a male above 50 experiences symptoms like blood in the urine and frequent
urination a urologist carries out the following tests which include Prostate Specific
Antigen (PSA) and Digital Rectal Examination (DRE). Because high concentrations
of PSA can also be linked to false positive results which causes unnecessary anxiety
to patients, it is not highly recommended. Following a positive PSA test, a DRE is
performed to determine the need for a biopsy.[4]Due to the highly invasive nature of
biopsies, the results from the above tests are first sent to a radiologist for Magnetic
Resonance Imaging (MRI) scan for detection of cancerous tissues in the prostate
before a biopsy is done. Recent advancements in imaging technology led to the
emergence of multiparametric Magnetic Resonance Imaging (mpMRI) which has
improved selectivity of cancerous regions by providing a more detailed picture of
the prostate than an MRI scan.