Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture

Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021

Xehetasun bibliografikoak
Egile Nagusiak:	Sharma, Tanmoyee, Tabassum, Zaharat, Banik, Ritu, Rahman, S.M.Arifur
Beste egile batzuk:	Mohsin, Abu S.M.
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2021
Gaiak:	Image segmentation U-Net++ Optical images Deep learning architecture Machine learning Artificial intelligence
Sarrera elektronikoa:	http://hdl.handle.net/10361/15142

Antzeko izenburuak

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ResInvolution: an involution-ResNet fused global spatial relation leveraging model for histopathological image analysis under federated learning environment
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Unsupervised semantic segmentation for localization of wetland area fluctuations
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An eﬃcient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling
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Prostate cancer detection using deep learning neural network with transfer learning approach
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Generation of realistic images from human drawn sketches using deep learning
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Detection and 3D visualization of Brain tumor using deep learning and polynomial interpolation
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