V. Thamilarasi

@srisaradacollege.ac.in

ASSISTANT PROFESSOR IN COMPUTER SCIENCE
SRI SARADA COLLEGE FOR WOMEN(AUTONOMOUS)



                    

https://researchid.co/v.thamilarasi

ASSISTANT PROFESSOR IN SRI SARADA COLLEGE FOR WOMEN(AUTONOMOUS)

EDUCATION

M.Sc, M.Phil, MCA., Ph.D IN COMPUTER SCIENCE

RESEARCH INTERESTS

MEDICAL IMAGE PROCESSING, DATA MINING

FUTURE PROJECTS

Efficient Classification by RESNET-50 and compare by deep learning architectures for lung chest X-ray images

I have already completed this work and i need collaboration for SCI publishing only


Applications Invited
Interested persons communicate me
3

Scopus Publications

Scopus Publications

  • Application of machine learning in chest X-ray images


  • Classification of Lung Chest X-Ray Images Using Deep Learning with Efficient Optimizers
    A. Asaithambi and V. Thamilarasi
    IEEE
    This paper investigates the application of several deep learning architectures such as VGG-16, VGG-19, ResNet-50, and Xception Net for lung chest X-ray images, with 5, 10, and 15 epochs, and different optimizers such as Adam, SGD, and RMSProp, and a learning rate of 0.0001. The investigation finds that when adaptive gradient is used, the VCG-16 architecture achieves 68% accuracy; VCG-19 achieves 67% accuracy; ResNet-50 achieves 98.67% accuracy; and the Xception Net architecture achieves less than 50% accuracy. With further experimentation using 5, 10, and 15 epochs and optimizers such as Adam, SGD, and RMSProp, a 100% accuracy was achieved with 15 epochs for the VGG-16, VGG-19, and ResNet-50 architectures. However, Xception Net has been able to achieve only 70% accuracy with these optimizers.

  • Automatic thresholding for segmentation in chest X-ray images based on green channel using mean and standard deviation


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