Search

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance. [ABSTRACT FROM AUTHOR]

Point-of-care testing promises to cut diagnostic and treatment delays by ensuring patients receive a management decision based on a diagnostic test within one encounter with a provider. Adding to STS work on diagnostics and the sociology of diagnosis, this paper examines the work involved in enacting point-of-care testing, and how technology and the embedded assumptions regarding patients feature in these enactments. Using focus group discussions with providers and patients in India, the results reveal overlaps, detours and frictions along diagnostic pathways. Diagnosing at point of care requires coordination work by providers and patients and alignment of diagnostic ensembles in which bodies, tools, knowledge, infrastructure, social relations and testing sites mutually configure each other. Patients do not always leave the point of care with one disease or diagnosis. In the process, they are both turned into objects as well as powerful actors. Contributions to STS theory and implications for global health innovation practices are discussed. [ABSTRACT FROM AUTHOR]

Long-term exposure to respirable dust containing silica leads to pneumoconiosis/silicosis. The disease is irreversible and incurable, and only preventive steps such as job rotation, use of personal protective equipment, etc., remain solutions to the problem. Under such a situation, early diagnosis or prediction may become very useful to control the disease. Biomarkers are biological parameters in blood serum that change their values with deposition of dust in the lung and onset of lung fi brosis. Since these biomarkers can help us to diagnose and in the prognosis of the disease before it is actually diagnosed by the conventional X-ray technique and lung function test used for diagnosis of silicosis. The present paper describes the various types of available biomarkers, their application and usefulness. The paper also suggests that further study of the behavior/level of these biomarkers on a specifi c subject with time may provide more useful information of a confi rmatory nature for prevention of dust-linked diseases. [ABSTRACT FROM AUTHOR]

Debate over `scientific culture' is often over-determined by universalistic and Eurocentric constructions of `modern science'. In this paper I have attempted to ground this debate through an analysis of the culture of magnetic resonance imaging (MRI) research in India. My analysis is based upon observations of and interviews with scientists working in the MRU and nuclear magnetic resonance (NMR) laboratories and government officials who regulate techno-scientific research in India. I argue that even though some common characteristics of scientific practices in India are discernible, we cannot attribute them to a historical cognitive or social aspects of the Indian society. We also cannot argue that such practices exist because of the absence of a `scientific community', whose members share `scientific values'. I draw on empirical studies of scientific knowledge and practice, as well as postcolonial studies, to show how particular practices with MRI and NMR in India, which have to do with scientific collaboration and patenting make perfect sense if they are seen in relation to global and national networks of power and administration. I argue that scientific cultures are dialectically related to networks of regulatory and laboratory practices, and that they change as the networks change, as we are beginning to witness in India. [ABSTRACT FROM AUTHOR]

Typically, the acquired renal ultrasound image includes a course of speckle noises. This paper primarily investigates an approach for the detection of renal calculi by processing those raw US images with the help of a meta-heuristic SVM classifier. One of the major downsides of involving Ultrasound images in medical analysis is the prevalence of Speckle Noises. An Adaptive Mean Median Filter approach has been introduced in the work to get rid of the speckle noises to the maximum extent ever in the literature. Segmentation is performed by employing conventional K-Means and GLCM features were extracted for classification using a meta-heuristic SVM classifier. The proposed methodology investigates with a Real-time Acquired Dataset of Mithra Scans, Tamilnadu, India comprises of 250 clinical Ultra-Sound Kidney Images of which 150 are having Calculi and the rest are Healthy. With the experimental results, the proposed meta-heuristic SVM classifier have performed better in noisy images while comparing with other conventional methods considered in the literature. It exhibits an Accuracy of 98.8% with a FAR rate of 1.8 for FRR as high as 3.3. The results clearly proposed that the novel AMM-PSO-SVM could be a promising technique for object or foreign body detection in a medical imaging application that uses ultrasound imaging. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is an eye disease, which affects the people who are all having the diabetes for more than 10 years. The ophthalmologist identifies when the dilated eye exam causes severe in any one of the following deviations in the retina: changes in blood vessels, leaking blood vessels, newly grown blood vessels, swelling of the macula, changes in the lens, and damages to the nerve tissue. It can eventually lead to vision loss. The early detection of DR prevents the cause of blindness. In this paper, we propose the retinal image segmentation and extraction of blood vessels by morphological processing, thresholding, edge detection, and adaptive histogram equalization. For the automatic diagnosis of DR from the fundus image, we also developed a network with the convolutional neural network architecture for accurately classifying its severity. By using high-end graphical processor unit (GPU), we trained this network on the publicly available dataset such as DRIVE, DIARETDB0, and DIARETDB1_v1, and the images collected from the Aravind Eye Hospital, Coimbatore, India. Our proposed CNN achieves a sensitivity of 98%, a specificity of 93%, and an accuracy of 96.9% containing a database of 854 images. [ABSTRACT FROM AUTHOR]