Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.
Non-Hodgkin lymphoma (NHL) is a hematological malignancy that requires effective pharmacotherapy for optimal management. There is limited information regarding Yemeni clinicians' knowledge and practice of NHL pharmacotherapy. This study aims to assess the knowledge and practice of physicians and nurses in Yemen regarding pharmacotherapy of NHL. A cross-sectional study was conducted in Sana'a, Yemen, from January 1, 2022, to January 31, 2023. Two self-administrated and validated questionnaires were distributed to 99 physicians and 164 nurses involved in pharmacotherapy for NHL in different oncology centers and units across Yemen. Convenience samples were used to recruit participants. A binary logistic regression analysis was performed to identify factors associated with nurses' and physicians' knowledge and practice. The correlation coefficient was used to examine the relationship between knowledge and practice. A total of 77 physicians and 105 nurses completed the questionnaires. The results showed that 54.3% of nurses and 66.2% of physicians had poor knowledge of NHL pharmacotherapy. In terms of practice, 83.8% of nurses and 75.3% of physicians exhibited poor practice regarding NHL pharmacotherapy. Multivariable logistic regression analysis identified that nurses who received sufficient information about chemotherapy displayed a significant association with good knowledge, while nurses working in the chemotherapy administration department were significant predictors of good practice. Among physicians, those working in the National Oncology Center (NOC) in Sana'a demonstrated good practice. Correlation analysis revealed a positive relationship between nurses' knowledge and their practice. The study's results confirm deficiencies in knowledge and practice of pharmacotherapy for NHL among physicians and nurses in Yemen. Efforts should be made to enhance their understanding of treatment guidelines and to improve patient care. Improvement in educational programs and training opportunities may contribute to improving patient outcomes in the management of NHL.