Multi-class Cervical Cancer Classification using Transfer Learning-based Optimized SE-ResNet152 model in Pap Smear Whole Slide Images

Among the main factors contributing to death globally is cervical cancer, regardless of whether it can be avoided and treated if the afflicted tissues are removed early. Cervical screening programs must be made accessible to everyone and effectively, which is a difficult task that necessitates, among other things, identifying the population\u27s most vulnerable members. Therefore, we present an effective deep-learning method for classifying the multi-class cervical cancer disease using Pap smear images in this research. The transfer learning-based optimized SE-ResNet152 model is used for effective multi-class Pap smear image classification. The reliable significant image features are accurately extracted by the proposed network model. The network\u27s hyper-parameters are optimized using the Deer Hunting Optimization (DHO) algorithm. Five SIPaKMeD dataset categories and six CRIC dataset categories constitute the 11 classes for cervical cancer diseases. A Pap smear image dataset with 8838 images and various class distributions is used to evaluate the proposed method. The introduction of the cost-sensitive loss function throughout the classifier\u27s learning process rectifies the dataset\u27s imbalance. When compared to prior existing approaches on multi-class Pap smear image classification, 99.68% accuracy, 98.82% precision, 97.86% recall, and 98.64% F1-Score are achieved by the proposed method on the test set. For automated preliminary diagnosis of cervical cancer diseases, the proposed method produces better identification results in hospitals and cervical cancer clinics due to the positive classification results

Correlation of clinical and ultrasonographic features with histopathology in post-menopausal bleeding

Background: Postmenopausal bleeding is generally regarded as an ominous alarm of genital pathologies which requires a thorough evaluation clinically and pathologically to exclude carcinoma as the cause and ensure a benign pathology. This study aims at finding out whether clinical diagnosis and ultrasonographic features can be reliable parameters for the diagnosis of causes and whether the findings correspond with histopathology reports.Methods: This observational study was conducted in a tertiary care centre in Pondicherry between January 2018 to August 2019. 114 women were enrolled for whom detailed history taking and clinical examination was done. All the patients were subjected to transvaginal ultrasonography. Patients with clinically visible lesions on cervix and vulva were subjected to biopsy and the rest underwent fractional curettage and the sample was sent for histopathological examination. Finally, histopathology report was compared with clinical and ultrasonographic findings.Results: With endometrial thickness cut off of 4 mm, the sensitivity, specificity, positive predictive value and negative predictive value in predicting malignancy by ultrasonography were 100%, 12.3%, 4.5% and 100%. Histopathology showed atrophic endometrium (43.8%), endometrial hyperplasia (8%), endometrial polyp (7.9%) and endometrial carcinoma (3%). Clinical and ultrasonographic findings did not show any statistical correlation with histopathology.Conclusions: Authors conclude that clinical findings and ultrasonographic features do not correlate with histopathology in cases of postmenopausal bleeding for which atrophic endometrium was the commonest etiology. However, ultrasound should be done routinely before endometrial sampling as the sensitivity for predicting malignancy was 100% for endometrial thickness cut off of 4 mm

Dimensionality reduction and hierarchical clustering in framework for hyperspectral image segmentation

The hyperspectral data contains hundreds of narrows bands representing the same scene on earth, with each pixel has a continuous reflectance spectrum. The first attempts to analysehyperspectral images were based on techniques that were developed for multispectral images by randomly selecting few spectral channels, usually less than seven. This random selection of bands degrades the performance of segmentation algorithm on hyperspectraldatain terms of accuracies. In this paper, a new framework is designed for the analysis of hyperspectral image by taking the information from all the data channels with dimensionality reduction method using subset selection and hierarchical clustering. A methodology based on subset construction is used for selecting k informative bands from d bands dataset. In this selection, similarity metrics such as Average Pixel Intensity [API], Histogram Similarity [HS], Mutual Information [MI] and Correlation Similarity [CS] are used to create k distinct subsets and from each subset, a single band is selected. The informative bands which are selected are merged into a single image using hierarchical fusion technique. After getting fused image, Hierarchical clustering algorithm is used for segmentation of image. The qualitative and quantitative analysis shows that CS similarity metric in dimensionality reduction algorithm gets high quality segmented image

Delivery of a fetus with undiagnosed sacro coccygeal teratoma

Sacrococcygeal teratoma is the most common tumour of the fetus and neonate with an incidence of 1 in 40000 births. Here we describe the management of an undiagnosed sacrococcygeal teratoma, which is rare in this era

Giant dermoid cyst of ovary in postmenopausal woman: a case report

A 49 year, miltipara, post-menopausal woman complains of pain abdomen and breathlessness for one week. On abdominal examination, there was a firm mass. MRI showed multi-loculated cystic lesion suggestive of ovarian dermoid cyst. Patient underwent TAH and BSO.

Healthcare in continuum for an ageing population: national self monitoring or remote offshore monitoring for Australia?

Australia is a country, similar to other developed nations, confronting an ageing population with complex demographics. Ensuring continued healthcare for the ageing, while providing sufficient support for the already aged population requiring assistance, is at the forefront of the national agenda. Varied initiatives are with foci to leverage the advantages of lCTs leading to e-Health provisioning and assisted technologies. While these initiatives increasingly put budgetary constraints on local and federal governments, there is also a case for offshore resourcing of non-critical health services, to support, streamline and enhance the continuum of care, as the nation faces acute shortages of medical practitioners and nurses. However, privacy and confidentiality concerns in this context are a significant issue in Australia. In this paper, we take the position that if the National and state electronic health records system initiatives, are fully implemented, offshore resourcing can be a feasible complementary option resulting in a win-win situation of cutting costs and enabling the continuum of healthcare.<br /

Comparison between two layer and three layer repair of episiotomy

Background: The study was done to compare the two layer episiotomy suturing and three layer episiotomy suturing with regard to time taken to suture, number of suture material required and complications at suturing site postnatally.Methods: This study was conducted in the Gynaecology and Obstetrics department of Karnataka Institute of Medical Science, Hubli, Karnataka, India from 1st March 2015 to 30th April 2015.Results: The two layered technique required lesser suture material and lesser time compared to the three layer techhnique.Conclusions: In our study two layer techniques took lesser time and lesser suture material than three layer technique