Classification of squamous cell cervical cytology

Cervical cancer occurs significantly in women in developing countries every day and produces a high number of casualties, with a large economic and social cost. The World Health Organization, in the right against cervical cancer, promotes early detection screening programs by difeerent detection techniques such as conventional cytology (Pap), cytology liquid medium (CML), DNA test Human Papillomavirus (HPV), staining with dilute acetic acid and Lugol's iodine solution. Conventional cytology is the most used technique, being widely accepted, inexpensive, and with quality control mechanisms. The test has shown a sensitivity of 38% to 84% and a specificity of 90% in multiple studies and has been considered as the choice test for screening [14]. The cervical cancer is not a public health problems in developed countries since more than three decades, among others because of implementation of other tests such as the CML which has increased the sensitivity to a figures that vary between 76% and 99 %. This test in particular produces a thin monolayer of cells that are examined. In our countries this technique is really far from being applied because of its high cost. In consequence, the conventional cytology has remained in practice as the only possible examination of the cervix pathology. In this technique, a sample of cells from the transformation zone of the cervix is taken, using a brush or wooden spatula, spread onto a slide and fixed with a preservative solution. This sample is then sent to a laboratory for staining and microscopic examination to determine whether cells are normal or not. This task requires time and expertise for the diagnosis. Attempting to alleviate the work burden from the number of examinations in clinical routine scenario, some researchers have proposed the development of computational tools to detect and classify the cells of the transformation cervix zone. In the present work the transformation zone is firstly characterized using color and texture descriptors defined in the MPEG-7 standard, and the tissue descriptors are used as the input to a bank of binary classifiers, obtaining a precision of 90% and a sensitivity of 83 %. Unlike traditional approaches that extract cell features from previously segmented cells, the present strategy is completely independent of the particular shape. Yet most works in the domain report higher precision rates, the images used in these works for training and evaluation are really different from what is obtained in the cytology laboratories in Colombia. Overall, most of these methods are applied to monolayer techniques and therefore the recognition rates are better from what we found in the present investigation. However, the main aim of the present work was thus to develop a strategy applicable to our real conditions as a pre-screening method, case in which the method should be robust to many random factors that contaminate the image capture. A segmentation strategy is very easily misleaded by all these factor so that our method should use characteristics independently of the segmentation quality, while the reading time is minimized, as well as the intra-observer variability, facilitating thereby real application of such screening tools.Maestrí

Cervical Cancer Automated Screening Module - CervCancerScreening

There are more than 100 types of cancer and they are the leading causes of deaths all over the world. Many of them are fatal but many other can be treated if they are diagnosed early on, like the Cervical Cancer. This being said, the Cervical Cancer is still the second leading cause of female deaths in developing countries. The reason behind this is because of the lack of early diagnosis and sometimes even because lack of information. Since the women in this countries do not have gynecological surveillance, mainly because its cost, chances are high that the cancer grows to a state that can no longer be treated. When the surveillance actually exists, it is very expensive since the samples of the tests made need to be sent to another country with access to specific equipment. With the increasing techonological development, it is possible to create a less expensive tool to try to supress some of this problems. The tool will be used to assist the medical staff so that they have a first overview of the patient's cervical cancer results. Nevertheless, the ultimate decision on whether a person actually has or does not have cancer comes from the doctor.The objective of this thesis is to develop a tool that can support and be used by developing countries health facilities, so that they have the first impression about the situation of the patient by analysing the pictures of the samples gathered during the appointment. The main focus of the tool is to analyse and identify specific characteristics of the sample pictures in order to be able to give feedback about the existence of the cancer. In the end, the main objective is to spare those facilities sending the samples to other places and spend more money than they should, as the tool would give some insights about the risks and the need of further analyses

Machine Learning Techniques for Cervigram Image Analysis

Machine learning is a popular technology widely used to solve a lot of problems in various areas in recent decades. In this work, we applied machine learning techniques to the problems of medical image analysis, especially cervigram image analysis. Combined with techniques developed in computer vision, we represent cervigram image data in the form of a combination of texture feature vector and color feature vector. We treat the task of detecting Cervical Intraepithelial Neoplasia (CIN) level as a classification problem in the view of machine learning and apply several popular machine learning classifiers to predict the categories. Furthermore, under receiver operating characteristic (ROC) curve as our performance measure, we do a comprehensive comparison among seven machine learning classification algorithms to see which ones might be suitable models for this kind of problems. From our experiments, we conjecture that the machine learning techniques can be a useful tool and ensemble-tree based models like Random Forest, Gradient Boosting Decision Tree and Adaboost outperform other algorithms for this task