PRNet: Attention Induced Parameter Reduction For Computationally Efficient Diagnosis of Chest X-Rays (Record no. 16580)

MARC details
000 -LEADER
fixed length control field 01690nam a22001697a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.42
Item number S531P
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Sharma, Ranjeeta
245 ## - TITLE STATEMENT
Title PRNet: Attention Induced Parameter Reduction For Computationally Efficient Diagnosis of Chest X-Rays
Statement of responsibility, etc by Ranjeeta Sharma
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Computer Science and Technology
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages vii,10p.
Other physical details HB
500 ## - GENERAL NOTE
General note Most CNN models for chest x-ray diagnosis are computationally intensive both during training and testing. The computational burden may be an inhibiting factor towards the applicability of such models, especially in resource-constrained environments. We propose a computationally lightweight CNN architecture for identifying chest x-ray images with abnormalities. Because of its computational efficiency, our model may be useful even in resource-constrained environments. The proposed parameter-reduced network (PRNet) introduces a channel attention mechanism to achieve superior performance despite parameter reduction using depthwise convolution and squeeze and expand methods. Thus, PRNet helps to reduce model parameters and computational costs without compromising performance. The average AUC obtained using PRNet is as high as 95.66%. Rigorous experiments on publicly available chest x-ray datasets show the utility of the proposed model. Our model has approximately 0.065 million parameters and a size of less than 1 MB. This makes our model useful even with low-end desktop computers or smartphones.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Computer Science and Technology
Topical Term PRNet
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Paul, Angshuman
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Source of acquisition Full call number Accession Number Price effective from Koha item type
        Theses S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-04-01 Office of Academics 006.42 S531P TM00515 2024-06-28 Thesis