P
Prajoy Podder
Researcher at Bangladesh University of Engineering and Technology
Publications - 86
Citations - 1298
Prajoy Podder is an academic researcher from Bangladesh University of Engineering and Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 12, co-authored 73 publications receiving 531 citations. Previous affiliations of Prajoy Podder include Khulna University of Engineering & Technology & Khulna University.
Papers
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Journal ArticleDOI
Hybrid deep learning for detecting lung diseases from X-ray images
TL;DR: In this article, the authors proposed a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN, which is termed as VGG Data STN with CNN (VDSNet).
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Comparative Performance Analysis of Hamming, Hanning and Blackman Window
TL;DR: Comparing simulation results of different window, this paper has found Blackman window with best performance among them which is expected from the theory and found the same expected result.
Journal ArticleDOI
Data analytics for novel coronavirus disease
TL;DR: Different aspects of novel coronavirus disease (COVID-19) are described, visualization of the spread of the infection is presented, and the potential applications of data analytics on this viral infection are discussed.
Journal ArticleDOI
Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
Aditya Khamparia,Subrato Bharati,Prajoy Podder,Deepak Gupta,Ashish Khanna,Thai Kim Phung,Dang N. H. Thanh +6 more
TL;DR: The proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks and can be considered as an effective tool for radiologists to decrease the false negative and false positive rates of mammograms.
Proceedings ArticleDOI
Iris image compression using wavelets transform coding
Arnob Paul,Tanvir Zaman Khan,Prajoy Podder,Rafi Ahmed,M. Muktadir Rahman,Mamdudul Haque Khan +5 more
TL;DR: This paper has investigated the effects of compression particularly for iris image based on wavelet transformed image, using Spatial-orientation tree wavelet, Embedded Zero tree Wavelet and Set Partitioning in hierarchical trees (SPIHT), to identify the most suitable image compression.