| Description |
1 online resource (x, 377 pages) : illustrations (some color) |
| Series |
Studies in computational intelligence, 1860-949X ; volume 932 |
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Studies in computational intelligence ; v. 932. 1860-949X
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| Contents |
6G Communication Technology: A Vision on Intelligent Healthcare / Sabuzima Nayak and Ripon Patgiri -- Deep Learning-Based Medical Image Analysis Using Transfer Learning / Swati Shinde, Uday Kulkarni, Deepak Mane, and Ashwini Sapkal -- Wearable Internet of Things for Personalized Healthcare: Study of Trends and Latent Research / Samiya Khan and Mansaf Alam -- Principal Component Analysis, Quantifying, and Filtering of Poincaré Plots for time series typal for E-health / Gennady Chuiko, Olga Dvornik, Yevhen Darnapuk, and Yaroslav Krainyk -- Medical Image Generation Using Generative Adversarial Networks: A Review / Nripendra Kumar Singh and Khalid Raza -- Comparative Analysis of Various Deep Learning Algorithms for Diabetic Retinopathy Images / Neha Mule, Anuradha Thakare, and Archana Kadam -- Software Design Specification and Analysis of Insulin Dose to Adaptive Carbohydrate Algorithm for Type 1 Diabetic Patients / Ishaya Gambo, Rhodes Massenon, Terungwa Simon Yange, Rhoda Ikono, Theresa Omodunbi, and Kolawole Babatope -- An Automatic Classification Methods in Oral Cancer Detection / Vijaya Yaduvanshi, R. Murugan, and Tripti Goel -- IoT Based Healthcare Monitoring System Using 5G Communication and Machine Learning Models / Saswati Paramita, Himadri Nandini Das Bebartta, and Prabina Pattanayak -- Forecasting Probable Spread Estimation of COVID-19 Using Exponential Smoothing Technique and Basic Reproduction Number in Indian Context / Zakir Hussain and Malaya Dutta Borah -- Realization of Objectivity in Pain: An Empirical Approach / K. Shankar and A. Abudhahir -- Detail Study of Different Algorithms for Early Detection of Cancer / Prasenjit Dhar, K. Suganya Devi, Satish Kumar Satti, and P. Srinivasan -- Medical Image Classification Techniques and Analysis Using Deep Learning Networks: A Review / Arpit Kumar Sharma, Amita Nandal, Arvind Dhaka, and Rahul Dixit -- Protein Interaction and Disease Gene Prioritization / Brijendra Gupta -- Deep Learning Techniques Dealing with Diabetes Mellitus: A Comprehensive Study / Sujit Kumar Das, Pinki Roy, and Arnab Kumar Mishra -- Noval Machine Learning Approach for Classifying Clinically Actionable Genetic Mutations in Cancer Patients / Anuradha Thakare, Santwana Gudadhe, Hemant Baradkar, and Manisha Kitukale -- Diagnosis Evaluation and Interpretation of Qualitative Abnormalities in Peripheral Blood Smear Images--A Review / K. Suganya Devi, G. Arutperumjothi, and P. Srinivasan -- Gender Aware CNN for Speech Emotion Recognition / Chinmay Thakare, Neetesh Kumar Chaurasia, Darshan Rathod, Gargi Joshi, and Santwana Gudadhe |
| Summary |
This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed March 9, 2021) |
| Subject |
Medical informatics.
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Bioinformatics.
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Data mining.
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Computational intelligence.
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Computational Biology
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Data Mining
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Bioinformatics
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Computational intelligence
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Data mining
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Medical informatics
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| Form |
Electronic book
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| Author |
Patgiri, Ripon, editor.
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Biswas, Anupam, editor.
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Roy, Pinki, editor.
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| ISBN |
9789811597350 |
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9811597359 |
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