Real-time MRI lungs images revealing using Hybrid feedforward Deep Neural Network and Convolutional Neural Network

Issue title: Machine Learning Based Computational Bioinformatics for Healthcare Big Data

Guest editors: Muhammad Attique Khan, Gaurav Dhiman and Sathishkumar VE

Article type: Research Article

Authors: Karthick, M.^{a; *} | Samuel, Dinesh Jackson^b | Prakash, B.^c | Sathyaprakash, P.^d | Daruvuri, Nandhini^e | Ali, Mohammed Hasan^f | Aiswarya, R.S.^g

Affiliations: [a] Department of Information Technology, Nandha College of Technology, Tamilnadu, India | [b] Research Scientist Biomedical Engineering, University of California, Davis, CA, USA | [c] Department of Computing Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, India | [d] School of Computing, SASTRA Deemed to be University, Thanjavur, India | [e] Intel Corporation, IoTG Research and Development lab, Folsom, CA, USA | [f] Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghda, Iraq | [g] KPR Institute of Engineering and Technology, Uthupalayam, Tamil Nadu, India

Correspondence: [*] Corresponding author: M. Karthick, Department of Information Technology, Nandha College of Technology, Tamilnadu, India. E-mail: [email protected].

Abstract: This research focused on Real-time MRI lung images that were revealed using three grade processes by manipulating nanophotonics components, mapping by deep learning, machine learning, and pattern recognition. This research is Solving Magnetic resonance imaging of interstitial lung diseases with Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture. The feedforward deep neural network (ffDNN) and Convolutional Neural Network (CNN) techniques are used to Solving Magnetic resonance imaging of interstitial lung diseases on the nanophotonics components, deep learning, and machine learning Platform. The Proposed semiconductor monolithic integration approach employed for bio-Magnetic resonance imaging characterization using photonic crystal “Symptomatic Image Revealing” details of the resonant monolithic. The proposed machine-learning-based approach revealed characterizing multi-parameter design space of nanophotonic components using Nano-optic imagers. The Pattern Recognition for MRI was performed for lower dimensionality. Finally, the Hybrid feedforward Deep Neural Network (ffDNN) and Convolutional Neural Network (CNN) architecture for calculating the height and size of scatterers using the inverse design of the meta-optical structure. The temporal resolution assessment of image data pixel size 280x360 hyperspectral imaging temporal resolution is 25, and magnetic resonance imaging temporal resolution is 50. The Image distribution shows that phase shift and transmission are 2.78 degrees and at 95%. The result for the inverse design using CNN returns the efficient inverse design of test data that can be designed according to the required pressure distribution. Wavelength 1000 nanometer to 1600 machine learning method absorbance 40% and ffDNN absorbance 33%.

Keywords: Convolutional neural network, deep learning, machine learning, MRI lungs images, nanophotonics components mapping, pattern recognition

DOI: 10.3233/IDA-237436

Journal: Intelligent Data Analysis, vol. 27, no. S1, pp. 95-114, 2023