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Swarm Intelligence and Machine Learning Algorithms for Cancer Diagnosis
Published in Shikha Agrawal, Manish Gupta, Jitendra Agrawal, Dac-Nhuong Le, Kamlesh Kumar Gupta, Swarm Intelligence and Machine Learning, 2022
Pankaj Sharma, Vinay Jain, Mukul Tailang
Machine intelligence and CNN techniques show potential for prostate cancer detection and prognosis forecasting. There is a tremendous need for more study because there is very little information. There is a tremendous need for more study due to shortage of information. Diagnostic imaging, genetics, histopathological, and treatment each have the potential to enhance disease atomisation and, as a corollary, improving clinical treatment in the hospital. In the near future, the possibility of learning algorithms in prostate surgical intervention will improve planning and operation outcomes, both in terms of increasing patient outcomes and in terms of teaching and assessing surgical abilities.
Big Data-Based Decision Support Systems for Hadron Therapy
Published in Manjit Dosanjh, Jacques Bernier, Advances in Particle Therapy, 2018
Yvonka van Wijk, Cary Oberije, Erik Roelofs, Philippe Lambin
Modern health care aspires to optimise personalised cancer therapy and faces many challenges in this respect. The increase in available treatment options and the diversity in patients prove incredibly problematic for individualised decision making. However, DSS developed using RLHC have the potential to bring us one step closer to realising that goal. An essential step that needs to be taken is the standardisation of data acquisition, including data concerning treatment, clinical features, imaging, genetics and outcome. Also, the clinical assessment of developed DSS is critical, as well as standardising the development of robust prediction models.
Advance Methods
Published in Atsushi Kawaguchi, Multivariate Analysis for Neuroimaging Data, 2021
This makes it possible to use high-dimensional data such as laboratory data (Yoshida et al., 2018) or genetic data (Imaging genetics, Nathoo et al., 2019) as explanatory variables, with images as objective variables. Thus, instead of clinical information, image data can be an objective variable, so it is also called an intermediate phenotype.
A novel solution of an elastic net regularisation for dementia knowledge discovery using deep learning
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Kshitiz Shrestha, Omar Hisham Alsadoon, Abeer Alsadoon, Tarik A. Rashid, Rasha S. Ali, P.W.C. Prasad, Oday D. Jerew
(J. J. Liu et al., 2016) developed the artificial neural network that uses multi-view fusion that combines with multiple estimators. This research paper provides a two-stage binary classification, i.e., Alzheimer’s Disease and Normal Control and Mild Cognitive Impairment and Normal Control. Ensemble learning layer and the soft-max layer is present in it to predict either the given subject is Alzheimer’s Disease or Mild Cognitive Impairment. Among the three views Magnetic Resonance Imaging + Positron Emission Tomography gives the best result. However, the proposed solution only focuses on binary classification, so it is necessary to extend the framework to the multi-class classification problem. (W. J. Niessen, 2016) studied the strong influence of genetics in the brain for knowledge discovery from Magnetic Resonance Imaging images. The author uses the extracted data by data references, the ageing model, and computer-aided for the diagnosis. The proposed solution makes analyzes to the brain Magnetic Resonance Imaging images in various aspects of a brain using the methodology like Imaging genetics, Ageing brain models, quantitative imaging biomarker, and Machine learning. However, the proposed solution has not done any measurement which should be considered to get the accuracy for comparison purposes.
Role of data science in managing COVID-19 pandemic
Published in Indian Chemical Engineer, 2020
Nikita Saxena, Priyanka Gupta, Ruchir Raman, Anurag S. Rathore
Data science technologies today are assisting medical science in reaching new milestones in medical imaging, genetics and genomics, drug discovery, patient–customer assistance, and predictive medicine. COVID-19 has put this in a spotlight. Data analytics has been successfully used to monitor real time disease outbreak, forecasting, and spotting real time trends for governments, health organisations, and society in general [1]. Technological advancements have allowed us to extract data from wet lab experiments, as well as imaging based (radiomics) and sensor based (from wearable sensors) systems. Data science field can be categorised into data management, data visualisation and statistical machine learning. Each category has techniques that can be used for organisation, sorting, processing and enabling real time data analysis. At present, chemical engineers can help in managing COVID-19 response using suitable data analytical techniques. These techniques are generally used to exploit the correlations in the datasets due to the mass transfer, energy transfer and basic thermodynamics [2,3] and can be implemented for process modelling [4], diagnostics [5] and predictions [6,7]. In comparison to the previous outbreaks, open source datasets for countries and cities have been made widely available for COVID-19. Combining these with the socio-economic factors, researchers have been engaged in mathematical modelling and use of artificial intelligence (AI) [8,9]. Applications in the field of risk assessment, diagnostics, modelling, contact tracing, economic and logistic planning, understanding effect of government policies and social interventions have helped us in gaining insights into the pandemic. The ongoing research to detect the anomalies of COVID-19, besides the diagnostics and therapeutics, is around detection of disease using CT scans/X-rays, data mining to collate information from social media and patient records, diagnostics based on lungs and respiratory sound analysis in addition to speech and sound processing. The use of modelling techniques has become particularly useful during the COVID-19 pandemic for forecasting of trends and apply it for anticipating resource requirements, informed policy making, and ensuring adequate non-pharmaceutical interventions (NPIs). The present article aims to review the various ways in which modelling and artificial intelligence are being used to handle the COVID-19 situation efficiently and effectively.