According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.
Serratiopeptidase was effective for eradicating infection caused by biofilm-forming bacteria in this experimental animal model. The antibiofilm property of the enzyme may enhance antibiotic efficacy in the treatment of staphylococcal infections.
Kyphosis and consequent traction of the lumbar spine is one of the causes for increased epidural fibrosis formation after laminectomy. On the contrary, establishment of lordosis and relaxation of the lumbar spine decreased the scar tissue formation in rats.
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