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Multiclass classification of skin cancer lesions using convolutional neural networks

By: Sevda, Sahil

Material type: Text

TextPublisher: Hamirpur : National Institute of Technology, 2023Description: viii, 42pDDC classification: 621.38 Dissertation note: Master of Technology, Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Himachal Pradesh, India - 177005. 2023 Guide / Supervisor: Kumar, Ashok

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Item type	Current location	Call number	Status	Date due	Barcode
Thesis or Dissertation	Central Library On Display	621.38 SEV (Browse shelf)	Available		TH-1948

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Previous	621.38 SAT Investigation of fully depleted soi mosfet for low power digital circuits	621.38 SAW Performance analysis of noma- based visible light communication systems	621.38 SAX Unsupervised machine translation and text augmentation for resource-rich and low resource indic languages	621.38 SEV Multiclass classification of skin cancer lesions using convolutional neural networks	621.38 SHA Analysis of underlap fully depleted strained soi mosfet	621.38 SHA Modeling and analysis of gaussian channel junctionless finfet for sram application	621.38 SHA Comparative study and performance evolution of proposed waveform techniques for 5g wireless communications	Next

Master of Technology, Department of Electronics and Communication Engineering, National Institute of Technology Hamirpur, Himachal Pradesh, India - 177005. 2023

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Multiclass classification of skin cancer lesions using convolutional neural networks

By: Sevda, Sahil

Browsing Central Library shelves, Shelving location: On Display Close shelf browser