Aim:To evaluate the cytomorphometric features in fine needle aspiration cytology (FNAC) of thyroid lesions.Materials and Methods:FNAC of 36 thyroid cases was reviewed. The study included 10 cases each of follicular lesion, adenomatous goiter, papillary carcinoma, 4 cases of medullary carcinoma and 2 cases of anaplastic carcinoma. Their ages ranged from 28 to 50 years, and there were nine females and one male. Morphometric analysis was done on aspiration smears from 36 thyroid lesions. Hematoxylin and Eosin stained smears were examined using image analyzer Proplus V software. Morphological parameters measured included mean nuclear diameter, mean nuclear perimeter, mean nuclear area, circular rate, largest to smallest dimension ratio (LS ratio) and coefficient of variation of nuclear area (NACV).Statistical Analysis:Statistical evaluation was carried out using the analysis of variance (ANOVA) test for the five variables, both within the group and in between the groups. The result was considered significant when P < 0.05.Results:The follicular carcinomas had higher LS ratio than patients with adenomatous goiters. Mean nuclear diameter and the mean nuclear perimeter were higher in anaplastic carcinomas when compared to other subtypes and were the least for follicular neoplasms.Conclusion:When correctly applied, quantitative estimation of cytological nuclear features can play an important role in preoperative assessment and can complement morphological features in thyroid lesions.

Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.

A 40-year-old woman presented to the Gynaecology Out Patients Department with intermittent lower abdominal pain which was there since past six months. The pain was dragging in nature and it increased over the past two weeks. Her past history and family history were uneventful. On examination, her general condition and laboratory investigations were found to be within normal limits. Per vaginal examination revealed a bulky cervix.

Novel methods and materials are used in healthcare applications for finding cancer in various parts of the human system. To select the most suitable therapy plan for individuals with domestically progressed cervical cancer, robustness metrics are required to estimate their early phase. The goal of the research is to increase the effectiveness of cervical cancer patients' detection by using deep learning-based radiomics assessment of magnetic resonance imaging (MRI). From March 2016 to November 2019, 125 patients with early-stage cervical cancer provided 980 dynamic X1 contrast-enhanced (X1DCE) and 850 X2 weighted imaging (X2WI) MRI images for training and testing. A convolutional neural network model was used to estimate cervical cancer state based on the specified characteristics. The X1DCE exhibited high discriminative outcomes than X2WI MRI in terms of prediction ability, as calculated by the confusion matrix assessment and receiver operating characteristic (ROC) curve approach. The mean maximum region under the curve of 0.95 was found using an attentive ensemble learning method that included both MRI sequencing (Sensitivity = 0.94, Specificity = 0.94, and accuracy = 0.96). Whenever compared with conventional radiomic approaches, the results show that a variety of radiomics based on deep learning might be created to help radiologists anticipate vascular invasion in patients with cervical cancer before surgery. Based on radiomics technique, it has proven to be an effective tool for estimating cervical cancer in its early stages. It would help people choose the best therapy method for them and make medical judgments.

Background: Like many other real world applications, the machine learning system of anaphora resolution also struggles with skewed data. The problem of imbalanced classes occurs with classification task where there a huge difference exists in the number of instances among the involved classes.Objectives: The proposed framework intends to remove the imbalance first between positive and negative class instances before classifying them by KBUS that makes use of cognitive knowledge about the language and analysis is done at attribute level. Method: Nine pruning rules are crafted by KBUS(KNN Based Under Sampling) for TDIL dataset. Findings: During experimentation, number of positive instances are increased from 5.32% to 43.95%, whereas the number of negative instances are decreased from 94.68% to 56.05%. Loss ratio of positive and negative instance is 1:112. Finally the pruned dataset is classified by a list of classifiers namelyNaïve Bayes, SVM, Random forest, decision tree and k-NN. Novelty: Classifier results are discussed in two perspectives: Firstly the number of input instances and secondly the performance improvement achieved after pruning. It is adduced that pruning shows a remarkable improvement for all the classifiers. The proposed system produced an encouraging result as 78% of f-measure for k-NN and 77% for decision tree. Performance is presented in a comparative manner before and after pruning and the improvement of fmeasureranges from 13% (k-NN) to 41% (Random Forest). Thus this work has come up with a machine learning model to resolve Tamil anaphoric situations effectively in an imbalanced classification environment.