Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. The texture features will be used to classify the ROIs as either masses or non-masses. In this study normal breast images and breast image with masses used as the standard input to the proposed system are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. In MIAS database, masses are grouped into either spiculated, circumscribed or ill-defined. Additional information includes location of masses centres and radius of masses. The extraction of the textural features of ROIs is done by using gray level co-occurrence matrices (GLCM) which is constructed at four different directions for each ROI. The results show that the GLCM at 0º, 45º, 90º and 135º with a block size of 8X8 give significant texture information to identify between masses and non-masses tissues. Analysis of GLCM properties i.e. contrast, energy and homogeneity resulted in receiver operating characteristics (ROC) curve area of Az = 0.84 for Otsu’s method, 0.82 for thresholding method and Az = 0.7 for K-mean clustering. ROC curve area of 0.8-0.9 is rated as good results. The authors’ proposed method contains no complicated algorithm. The detection is based on a decision tree with five criterions to be analysed. This simplicity leads to less computational time. Thus, this approach is suitable for automated real-time breast cancer diagnosis system

Background: Most postpartum women are prescribed at least one medication; so the safety of the medication is a major concern. In 2017, 11% of 815 questions received by the Pharmacy Drug Information Services at seven clinics in Dungun is related to medication use in breastfeeding. Thus, this study was carried out to evaluate the attitudes of healthcare providers (HCPs) and to investigate the effect of knowledge about medication use in breastfeeding among HCPs; pre- and post-educational intervention.Methods: An interventional study was carried out among medical officers, assistant medical officers, pharmacists and pharmacist assistants from seven clinics in the district of Dungun, Malaysia. The questionnaires were distributed during pre- and post-intervention period. The interventions in this study included continuous medical education (CME) and the use of a booklet regarding medication use for breastfeeding women. The data collected were analyzed using Statistical Package for Social Studies (SPSS) and presented as frequencies, means, and standard deviations.Results: Fifty HCPs were enrolled in this study and over 20% of them advised mothers to discontinue breastfeeding whenever they are prescribed any medication. The knowledge of HCPs about medication use in breastfeeding women was significantly improved (Z= -5.917, p<0.001) following the educational intervention.Conclusion: CME and a simplified booklet appeared to have a positive impact on the knowledge of HCPs regarding medication use in breastfeeding