28 research outputs found
Unleashing PET/CT Habitat Imaging Potential: Elevating Recurrence Prediction in NSCLC with ctDNA and Radiogenomics Insights
No full text<p><span>Habitat imaging subtypes derived from CT and PET scans obtained at multiple centers demonstrate value for predicting disease recurrence following curative surgery. This repository enables building a habitat imaging model that integrates CT and PET images and over segments the fused tumor region of each patient into super pixels. At population level, these superpixels are clustered using Louvain clustering algorithm. From the eight clusters identified, ninety-two MSI (multiregional spatial interaction) features are extracted to measure intratumoral spatial heterogeneity.</span></p>
A computer aided detection sytem for the evaluation of breast cancer
No full textFor many years, breast cancer has been one of the leading causes of death among women. Early detection of breast cancer is the key to improve the survival rate. Mammography is currently the most efficient tool for early detection of breast cancer. A large number of digital mammograms generated each year needs accurate and fast interpretation of images. The drawback of mammography is that the digital mammograms are difficult images to be interpreted visually. Non-cancerous lesions can be misinterpreted as a cancer (false-positive value), while cancers may be missed (false-negative value). A Computer-Aided Detection (CAD) system can help radiologists in this difficult task of interpreting digital mammograms. Though there are many CAD techniques developed today, clinical success depends on CAD having a high sensitivity, specificity and accuracy and the reader taking appropriate action when interpreting the CAD prompts. This balance is not easy to achieve. The motivation behind this research work is to develop a CAD system with high accuracy, sensitivity and specificity in the range of 95% to 100%. The work plan of the proposed research consists of four stages: The first stage involves the development of pre-processing methods for contrast enhancement of digital mammograms.The second stage of the proposed CAD is the development of suitable algorithms for segmentation of regions of interest using Otsu algorithm and data clustering techniques. The third stage involves the selection and extraction of image features tailored for the analysis of digital mammograms. Thirteen textural features for four spatial orientations: 0°, 45°, 90° and 135° are extracted from Gray-level co-occurrence matrices (GLCM) for the purpose of image analysis. The last stage is the evaluation of Neural network architectures and Support Vector Machines (SVM) aimed at using CAD system as a decision making aid for automatic detection of breast cancer. The Mammographic Image Analysis Society (MIAS) database of digital mammograms is used to evaluate the effectiveness of the developed CAD system. The overall performance of the developed system is compared with previously established methods based on the performance measures such as accuracy, specificity and sensitivity.
Keyword Extraction Using Particle Swarm Optimization
No full textAbstractWithout formal structure data are those that have no prearranged form or structure and are full of textual data. Typical unstructured systems include emails, reports, telephone or messaging conversations, etc. The main goal of this work is to extract the keywords from a conversation using particle swarm optimization. Keywords are grouped together under their classification and then suggested to the user. In existing work, using diverse keyword extraction, to find topic modelling information, representation of the main topics of transcript and diverse keyword selection. It maximizes the coverage of topics that are automatically recognized in transcript of conversation fragment. Once a set of keywords is extracted, it is clustered according to their user queries and recommended to the user. At the end of result, a single implicit query cannot improve user's satisfaction with the recommended documents. So, swarm intelligence technique is to be applied, it will minimize redundancy in a short list of Keywords and provide accurate query result compared to greedy algorithm
Technological Singularity in Sujatha Ranganathan’s En Iniya Iyanthira and Meendum Jeeno
No full textThe research paper aims at exploring the narrative aesthetics of Tamil science fiction in which the author takes twenty-first-century politics in India within the context of technological singularity. The article presents the political situation and totalitarianism in the age of technological singularity. The research focuses on the social impacts of artificial intelligence’s ability to read, learn, think, and act against its pre-programmed mechanism. A robotic dog struggles to restore a democratic political system from autocracy. The dystopian fictions “En Iniya Iyanthira” and “Meendum Jeeno” written by Sujatha Ranganathan depict the cognitive power of super intelligence behind a woman’s political actions to protect the people of India from exploitation, and corruption to create a better future. The paper demonstrates what a world without individual freedom looks like under the digital surveillance system of a totalitarian regime. The paper raises the question of what happens when a robot develops its rationality and mimics human behaviour. In these fictions, humans attempt to destroy the robotic dog. The robotic dog reaches a standard where nothing can destroy it. The paper explores the ways the robotic dog gains the knowledge to understand and practice the concept of humanity. The paper concludes with the post-humanistic conflicts between a woman and a robotic dog in emotional, ethical, and political aspects
Molecular docking and spectroscopic investigations aided by density functional theory of Parkinson’s drug 2-(3,4-dihydroxyphenyl)ethylamine
No full textSurface modification of CZTS nanoparticles using reflux method for effective utilizing absorber material
No full textPerformance Analysis of Farrow Structure Based FBMC-OQAM System
No full textAbstractFarrow structure is used in the efficient implementation of high order filters. The number of unknown coefficients is much less in Farrow structure based implementation, in comparison with the direct form implementation of FIR filters. Some predefined multipliers can also be used in this method. Since they are known apriori they will not add much to the complexity of the system. It is seen that a relatively strong correlation exists among the adjacent impulse response coefficients of the frequency selective filters. This fact is exploited in the Farrow structure to reduce the number of multipliers required for the implementation of desired filter. And these Farrow coefficients are used for representing the polyphase components of the desired filter. This Farrow structure based prototype filter is used for implementing an FBMC-OQAM system. BER performance of Farrow structure based FBMC-OQAM system is studied and found comparable with that of existing FBMC-OQAM system
Performance Improvement of Multicarrier Systems Using Wavelet Filter Banks
No full textAbstractThe need for higher data rates with increased bandwidth efficiency has focussed the search for techniques which deliver better results than conventional Orthogonal Frequency Division Multiplexing (OFDM) system. A wavelet filter bank system is investigated as a multicarrier modulation system (MCM). Such a system is found to be flexible, efficient and has many advantages over the present OFDM systems. This paper deals with identifying the suitability of different wavelet families, which can be used to improve the performance parameters of existing systems. Different wavelets families Daubechies, Meyer and Battle-Lemarie, are used as filter coefficients for wavelet based OFDM system and it is found that Daubechies wavelet (Db4) based multicarrier system outperforms the other two
Automated Image Quality Evaluation of Structural Brain Magnetic Resonance Images using Deep Convolutional Neural Networks
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