S
Sheifali Gupta
Researcher at University Institute of Engineering and Technology, Panjab University
Publications - 137
Citations - 812
Sheifali Gupta is an academic researcher from University Institute of Engineering and Technology, Panjab University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 6, co-authored 68 publications receiving 175 citations. Previous affiliations of Sheifali Gupta include Chitkara University & Singhania University.
Papers
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Deep Neural Networks for Medical Image Segmentation
TL;DR: This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks, and examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks.
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Detection of snow/ice cover changes using subpixel-based change detection approach over Chhota-Shigri glacier, Western Himalaya, India
TL;DR: In this paper, a subpixel-based change detection (SCD) approach is proposed, aiming to identify the transition zones (mixed pixels) between the two class categories, which involves the integration of subpixel classification and change vector analysis (CVA) to define the changes in the form of magnitude and direction between two multitemporal dates at the subpixel level.
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Deep Learning Model for the Automatic Classification of White Blood Cells
TL;DR: A deep learning (D.L) model is implemented that uses the DenseNet121 model to classify the different types of white blood cells (WBC) and has outperformed with batch size 8 as compared to other batch sizes.
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Modified U-NET Architecture for Segmentation of Skin Lesion
Vatsala Anand,Sheifali Gupta,Deepika Koundal,Soumya Ranjan Nayak,Paolo Barsocchi,Akash Kumar Bhoi +5 more
TL;DR: A modified U-Net architecture is proposed by modifying the feature map’s dimension for an accurate and automatic segmentation of dermoscopic images and evaluated the effectiveness of the proposed model by considering several hyper parameters such as epochs, batch size, and the types of optimizers.
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Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images
TL;DR: In this paper , a fusion model is proposed with the integration of the U-Net and Convolution Neural Network model for skin disease classification, which has outperformed on Adadelta optimizer with an accuracy value of 97.96%.