a b s t r a c tObjective: This paper presents an innovative idea of applying a hybrid ensemble technique i.e. ensemble of ensemble methods for improving the predictive performance of Artificial intelligence based system for screening of cervical cancer by characterization and classification of Pap smear images. Methodology: Papanicolaou smear (also referred to as Pap smear) is a microscopic examination of samples of human cells scraped from the lower, narrow part of the uterus, called the cervix. A sample of cells after being stained by using Papanicolaou method is analyzed under microscope for the presence of any unusual developments indicating any precancerous and potentially precancerous changes. Abnormal findings, if observed are subjected to further precise diagnostic subroutines. Examining the cell images for abnormalities in the cervix provides grounds for provision of prompt action and thus reducing incidence and deaths from cervical cancer. It is the most popular technique used for screening of cervical cancer. Pap smear test, if done with a regular screening programs and proper follow-up, can reduce cervical cancer mortality by up to 80% [1]. The contribution of this paper is that we have pioneered to apply hybrid ensemble technique to screen cervical cancer by classification of Pap smear data. The hybrid ensemble designed in this work has also presented an idea to use an ensemble of ensemble techniques. Using such a technique, the classification potentials of individual algorithms are fused together to gain greater classification accuracy. In addition to this we have also presented a comparative analysis of various artificial intelligence based algorithms for screening of cervical cancer. Results:The results indicate that hybrid ensemble technique is an efficient method for classification of Pap smear images and hence can be effectively used for diagnosis of cervical cancer. Among all the algorithms implemented, the hybrid ensemble approach outperformed & expressed an efficiency of about 96% for 2-class problem and about 78% for 7-class problem. The results when compared with the all the standalone classifiers were significantly better for both 2-class and 7-class problems.

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Objective: The primary objective of this research work is to develop a novel benchmark database of digitized and calibrated, cervical cells obtained from slides of Papanicolaou smear test, which is done for screening of cervical cancer. This database can serve as a potential tool for designing, developing, training, testing and validating various Artificial intelligence based systems for prognosis of cervical cancer by characterization and classification of Papanicolaou smear images. The database can also be used by other researchers for comparative analysis of working efficiencies of various machine learning and image processing algorithms. The database can be obtained by sending a request to the corresponding author. Besides developing a rich machine learning database we have also presented a novel artificial intelligence based hybrid ensemble technique for efficient screening of cervical cancer by automated analysis of Papanicolaou smear images. Methodology:The correct and timely diagnosis of cervical cancer is one of the major problems in the medical world. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. Papanicolaou smear (also referred to as Pap smear) is a microscopic examination of samples of human cells scraped from the lower, narrow part of the uterus, called cervix. A sample of cells after being stained by using Papanicolaou method is analyzed under microscope for the presence of any unusual developments indicating any precancerous and potentially precancerous developments. Abnormal findings, if observed are subjected to further precise diagnostic subroutines. Examining the cell images for abnormalities in the cervix provides grounds for provision of prompt action and thus reducing incidence and deaths from cervical cancer. It is the most popular technique used for screening of cervical cancer. Pap smear test, if done with a regular screening programs and proper follow-up, can reduce cervical cancer mortality by up to 80%. The contribution of this paper is that we have created a rich machine learning database of quantitatively profiled and calibrated cervical cells obtained from Papsmear test slides. The database so created consists of data of about 200 clinical cases (8091 cervical cells), which have been obtained from multiple health care centers, so as to ensure diversity in data. The slides were processed using a multi-headed digital microscope and images of cervical cells were obtained, which were passed through various data preprocessing subroutines. After preprocessing the cells were morphologically profiled and scaled to obtain separate quantitative measurements of various features of cytoplasm and nucleus respectively. The cells in the database were carefully classified in different corresponding classes according to latest 2001-Bethesda system of classification, by technicians. In addition to this, we have also pioneered to apply a novel hybrid ensemble system to this database in order to evaluate the effect...

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Objectives Pancreatic cancer (PC) is a very lethal malignancy with a scarcity of treatment options. Although erlotinib- and gemcitabine-based treatments have been approved for PC, their effectiveness is limited. The present study is aimed at exploring the molecular and epigenetic mechanisms of anticancer activities of homoharringtonine (HHT) and its interaction with erlotinib to develop a potential therapeutic strategy for PC. Methods The RT-qPCR, western blotting, immunofluorescence and expression-vectors and oligonucleotide transfection were employed to determine the expression characteristics of onco-factors. Anticancer activities were determined by MTT, colony forming, and flowcytometric analysis. Dual luciferase assay was conducted to confirm putative target of miR-130b-3p. In-vivo experiments were followed by immunohistochemical assay. Key findings The EphB4/JAK2/STAT3 pathway drives the growth and proliferation of PC through induction of prosurvival factors and cell cycle mediators. HHT directly and epigenetically via miR-130b-3p targets EphB4, leading to downregulation of JAK2/STAT3 pathway. The inactivation of STAT3 results in diminution of antiapoptotic factors and cell cycle mediators. HHT also enhances the anticancer activity of erlotinib. Conclusions HHT demonstrates potential anticancer activities in PC by downregulating EphB4/JAK2/STAT3 signalling. HHT also produces synergistic effects with erlotinib.