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| 1 | Erectile function following transurethral prostatectomy显示文摘 | Hanbury DC Sathia KK Refent B | 1995 | Br J Urol1995，15，6： | 1 |
| 2 | Basement membrane-dependent modification of phenotype and gene expression in human retinal pigment epithelial ARPE-19 cells显示文摘 | Turowski P Adamson P Sathia J | 2004 | Investigative Ophthalmology & Visual Science2004，45，： | 1 |
| 3 | Pharyngeal gonorrhoeais dual therapy the way forward?显示文摘 | Sathia L Ellis B Phillip S | 2007 | Int J STD AIDS2007，18，9： | 1 |
| 4 | Basement membrane-dependent modification of phenotype and gene expression in human retinal pigment epithelial ARPE-19 cells显示文摘 | Turowski P Adamson P Sathia J Zhang JJ Moss SE Aylward GW | 2004 | Invest Ophthalmol Vis Sci2004，45，8： | 1 |
| 5 | Pericardiectomy for Constrictive Pericarditis: A Clinical, Echocardiographic, and Hemodynamic Evaluation of Two Surgical Techniques显示文摘 | Ujjwal K. Chowdhury Ganapathy K. Subramaniam A. Sampath Kumar Balram Airan Rajvir Singh Sachin Talwar Sandeep Seth Pankaj K. Mishra Kizakke K. Pradeep Siddhartha Sathia Panangipalli Venugopal | 2006 | The Annals of Thoracic Surgery2006，，2： | 1 |
| 6 | Erectile function following transurethral prostatectomy 显示文摘 | Hanbury D C Sathia K K Refent B | 1995 | Br J Urol1995，15，6： | 1 |
| 7 | Erectile function following transurethral pros-tatectomy显示文摘 | Hanbury DC Sathia KK | | 0，，： | 1 |
| 8 | Histopathology of the right ven- tricular outflow tract and its relationship to clinical outcomes and ar- rhythmias in patients with tetralogy of Fallot 显示文摘 | Chowdhury UK Sathia S Ray R | 2006 | J Thorac Cardiovasc Surg2006，132，2： | 1 |
| 9 | Deep learning and machine learning neural network approaches for multi class leather texture defect classification and segmentation显示文摘Modern leather industries are focused on producing high quality leather products for sustaining the market com-petitiveness. However, various leather defects are introduced during various stages of manufacturing process such as material handling, tanning and dyeing. Manual inspection of leather surfaces is subjective and inconsistent in nature;hence machine vision systems have been widely adopted for the automated inspection of leather defects. It is neces-sary develop suitable image processing algorithms for localize leather defects such as folding marks, growth marks, grain off, loose grain, and pinhole due to the ambiguous texture pattern and tiny nature in the localized regions of the leather. This paper presents deep learning neural network-based approach for automatic localization and classifica-tion of leather defects using a machine vision system. In this work, popular convolutional neural networks are trained using leather images of different leather defects and a class activation mapping technique is followed to locate the region of interest for the class of leather defect. Convolution neural networks such as Google net, Squeeze-net, RestNet are found to provide better accuracy of classification as compared with the state-of-the-art neural network architectures and the results are presented. | Praveen Kumar Moganam Denis Ashok Sathia Seelan | 2022 | Journal of Leather Science and Engineering2022，4，1： | 0 |
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