000			01690nam a22001697a 4500
082			_a006.42 _bS531P
100			_aSharma, Ranjeeta _945397
245			_aPRNet: Attention Induced Parameter Reduction For Computationally Efficient Diagnosis of Chest X-Rays _cby Ranjeeta Sharma
260			_aIIT Jodhpur _bDepartment of Computer Science and Technology _c2023
300			_avii,10p. _bHB
500			_aMost 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			_aDepartment of Computer Science and Technology _945398
650			_aPRNet _945399
650			_aMTech Theses _945400
700			_aPaul, Angshuman _945401
942			_cTH
999			_c16580 _d16580