PRNet: Attention Induced Parameter Reduction For Computationally Efficient Diagnosis of Chest X-Rays (Record no. 16580)
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000 -LEADER | |
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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 |
Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Permanent Location | Current Location | Shelving location | Date acquired | Source of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 |
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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 |