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331 results on '"Razzaque, Md. Abdur"'

56. Toward Efficient Packet Buffering and Congestion Control Approaches for Reliable Data Delivery in Mobile Ad Hoc Network

Author

Hassan, Mohammad Mehedi, Alamri, Atif, Razzaque, Md. Abdur, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pathan, Mukaddim, editor, Wei, Guiyi, editor, and Fortino, Giancarlo, editor

Published
2013
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61. A Fast Algorithm to Calculate Powers of a Boolean Matrix for Diameter Computation of Random Graphs

Author

Razzaque, Md. Abdur, Hong, Choong Seon, Abdullah-Al-Wadud, M., Chae, Oksam, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Nakano, Shin-ichi, editor, and Rahman, Md. Saidur, editor

Published
2008
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62. Reliable Event Detection and Congestion Avoidance in Wireless Sensor Networks

Author

Mamun-Or-Rashid, Md., Alam, Muhammad Mahbub, Razzaque, Md. Abdur, Hong, Choong Seon, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Perrott, Ronald, editor, Chapman, Barbara M., editor, Subhlok, Jaspal, editor, de Mello, Rodrigo Fernandes, editor, and Yang, Laurence T., editor

Published
2007
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63. MC2DR: Multi-cycle Deadlock Detection and Recovery Algorithm for Distributed Systems

Author

Razzaque, Md. Abdur, Mamun-Or-Rashid, Md., Hong, Choong Seon, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Perrott, Ronald, editor, Chapman, Barbara M., editor, Subhlok, Jaspal, editor, de Mello, Rodrigo Fernandes, editor, and Yang, Laurence T., editor

Published
2007
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88. A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network

Author

Nahid, Abdullah-Al, Sikder, Niloy, Bairagi, Anupam Kumar, Razzaque, Md. Abdur, Masud, Mehedi, Kouzani, Abbas Z., Mahmud, M. A. Parvez, Nahid, Abdullah-Al, Sikder, Niloy, Bairagi, Anupam Kumar, Razzaque, Md. Abdur, Masud, Mehedi, Kouzani, Abbas Z., and Mahmud, M. A. Parvez

Abstract

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

Published
2020

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