Conventional pap smear (CPS) examination has been the mainstay for early detection of cervical cancer. However, its widespread use has not been possible due to the inherent limitations, like presence of obscuring blood and inflammation, reducing its sensitivity considerably. Automated methods in use in developed countries may not be affordable in the developing countries due to paucity of resources. On the other hand, manual liquid based cytology (MLBC) is a technique that is cost effective and improves detection of precursor lesions and specimen adequacy. Therefore the aim of the study was to compare the utility of MLBC with that of CPS in cervical cancer screening. A prospective study of 100 cases through MLBC and CPS was conducted from October 2009 to July 2010, in a Medical College in India, by two independent pathologists and correlated with histopathology (22 cases). Morphological features as seen through MLBC and CPS were compared. Subsequently, all the cases were grouped based on cytological diagnosis according to two methods into 10 groups and a subjective comparison was made. In order to compare the validity of MLBC with CPS in case of major diagnoses, sensitivity and specificity of the two methods were estimated considering histological examination as the gold standard. Increased detection rate with MLBC was 150%. The concordance rate by LBC/histopathology v/s CPS/histopathology was also improved (86% vs 77%) The percentage agreement by the two methods was 68%. MLBC was more sensitive in diagnosis of LSIL and more specific in the diagnosis of inflammation. Thus, MLBC was found to be better than CPS in diagnosis of precursor lesions. It provided better morphology with increased detection of abnormalities and preservation of specimen for cell block and ancillary studies like immunocytochemistry and HPV detection. Therefore, it can be used as alternative strategy for cervical cancer prevention in limited resource settings

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BackgroundImaging modalities in medicine gives complementary information. Inadequacy in clinical information made single imaging modality insufficient. There is a need for computer-based system that permits rapid acquisition of digital medical images and performs multi-modality registration, segmentation and three-dimensional planning of minimally invasive neurosurgical procedures. In this regard proposed article presents multimodal brain image registration and fusion for better neurosurgical planning.MethodsIn proposed work brain data is acquired from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) modalities. CT and MRI images are pre-processed and given for image registration. BSpline deformable registration and multiresolution image registration is performed on the CT and MRI sequence. CT is fixed image and MRI is moving image for registration. Later end result is fusion of CT and registered MRI sequences.ResultsBSpline deformable registration is performed on the slices gave promising results but on the sequences noise have been introduced in the resultant image because of multimodal and multiresolution input images. Then multiresolution registration technique is performed on the CT and MRI sequence of the brain which gave promising results.ConclusionThe end resultant fused images are validated by the radiologists and mutual information measure is used to validate registration results. It is found that CT and MRI sequence with more number of slices gave promising results. Few cases with deformation during misregistrations recorded with low mutual information of about 0.3 and which is not acceptable and few recorded with 0.6 and above mutual information during registration gives promising results.

Objectives:We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk.Materials and Methods:This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models.Results:The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively.Conclusion:Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

Background: The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease corresponding to it (coronavirus disease 2019; COVID-19) has been declared a pandemic by the World Health Organization. COVID-19 has become a global crisis, shattering health care systems, and weakening economies of most countries. The current methods of testing that are employed include reverse transcription polymerase chain reaction (RT-PCR), rapid antigen testing, and lateral flow testing with RT-PCR being used as the golden standard despite its accuracy being at a mere 63%. It is a manual process which is time consuming, taking about an average of 48 hours to obtain the results. Alternative methods employing deep learning techniques and radiologic images are up and coming. Methods: In this paper, we used a dataset consisting of COVID-19 and non-COVID-19 folders for both X-Ray and CT images which contained a total number of 17,599 images. This dataset has been used to compare 3 (non-pre-trained) CNN models and 5 pre-trained models and their performances in detecting COVID-19 under various parameters like validation accuracy, training accuracy, validation loss, training loss, prediction accuracy, sensitivity and the training time required, with CT and X-Ray images separately. Results: Xception provided the highest validation accuracy (88%) when trained with the dataset containing the X- ray images while VGG19 provided the highest validation accuracy (81.2%) when CT images are used for training. Conclusions: The model, VGG16, showed the most consistent performance, with a validation accuracy of 76.6% for CT images and 87.76% for X-ray images. When comparing the results between the modalities, models trained with the X-ray dataset showed better performances than the same models trained with CT images. Hence, it can be concluded that X-ray images provide a higher accuracy in detecting COVID-19 making it an effective method for detecting COVID-19 in real life.