Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network.

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies' major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

Clinical Outcomes Of Intra-Articular High Molecular Weight Hyaluronic Acid Injection For Hip Osteoarthritis- A Systematic Review And Meta-Analysis.

BACKGROUND: Osteoarthritis is the most common degenerative disease of the synovial joints in the elderly population with hip osteoarthritis as the second most commonly affected joint. A multitude of conservative treatments is used for pain relief and functional improvement including acetaminophen, NSAID, intra-articular corticosteroid, and viscosupplementation (VS). Different preparations of VS based on different molecular weights are commercially available. No systematic review or meta-analysis regarding the use of intra-articular high molecular weight hyaluronic acid (HMWHA) injection for the hip joint was published before. This review analyzes the efficacy of intra-articular HMWHA for hip osteoarthritis. METHODS: PubMed, Google Scholar, Cochrane Library for randomized trials describing the efficacy of HMWHA for hip osteoarthritis was searched. The search terms were osteoarthritis, hip joint, outcomes, viscosupplementation, and high molecular weight hyaluronic acid in different combinations. Standardized mean difference (SMD) in VAS for pain relief and Lequesne index for functional outcomes while risk ratio (RR) for complications was used for data pooling. RESULTS: Four studies comprising 185 and 189 patients in HMWHA and control groups were included, respectively. SMD for VAS and Lequesne index was -0.056 and -0.114, respectively while RR for complication was 0.879. CONCLUSIONS: Intra-articular HMWHA injection provided pain relief, functional improvement, and no severe complications on immediate short term basis. However, the results do not favor treatment with HMWHA over other treatment methods. Randomized trials are further necessary to provide data regarding comparisons between HMWHA for hip osteoarthritis concerning clinicians' convenience, compliance, duration of relief, and cost-effectiveness.