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

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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