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Arulmozhi, S.
- Effect of Leaf Galls of Piper nigrum Linn. Against Carageenan Induced Inflammation in Albino Rats
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Journal of Natural Remedies, Vol 7, No 2 (2007), Pagination: 229-233Abstract
Objective: To evaluate the anti-inflammatory activity of leaf galls of Piper nigrum Linn. against the carageenan induced paw oedema in Albino rats. Materials and methods: The benzene, chloroform and ethanol extracts of leaf galls of Piper nigrum Linn. were obtained by continuous soxhlet extraction. Each extract was assessed for anti-inflammatory activity in carageenan induced Albino rats by measuring the paw volume and ulcerogenic activity was also carried out. Results: All the extracts showed significant (P<0.05) anti-inflammatory activity in which ethanol extract showed prominent (P<0.01) effect. The extracts were devoid of ulcerogenic action. Conclusion: From the results, it is revealed that, the active ethanol extract of leaf galls of Piper nigrum is worthwhile to develop the bioactive principle for inflammatory disorders.Keywords
Piper nigrum, Antiinflammatory, Ulcerogenic- Comparative Food Intake Inhibitory Activity of Sida cordifolia L. and Withania somnifera L. in Rats
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Journal of Natural Remedies, Vol 7, No 2 (2007), Pagination: 289-293Abstract
Objective: The purpose of the present study was to evaluate food intake inhibitory activity of aqueous extract of Sida cordifolia (AESC) and alcoholic extract of Withania somnifera (AEWS). Both Sida cordifolia Linn. (Malvaceae) and Withania somnifera Linn. (Solanaceae) are widely growing medicinal plants and have been reported to possess number of medicinal properties. Materials and methods: The food intake inhibitory activity of different concentrations of AESC and AEWS (0.5% w/w, 1% w/w and 1.5% w/w) were evaluated by supplementing them with normal feed of rats for seven days, measuring their body weight and food intake daily and compared with the control. Results: There was a significant decrease in food intake (P<0.001) and body weight (P<0.01) with 1 % w/w and 1.5 % w/w of AESC while decrease in food intake and body weight with 0.5 % w/w of AESC was not significant. AEWS showed a significant (P<0.05) decrease in food intake only, but no significant decrease in body weight was observed with AEWS at any dose level. Conclusion: Both extracts were found to have significant food intake inhibitory activity. However when compared to AEWS, AESC was found to be more effective in reducing the food intake and bodyweight.- A Review: Deep Learning Techniques for Image Classification of Pancreatic Tumor
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1 Department of Computer Science, Chikkanna Government Arts College, IN
1 Department of Computer Science, Chikkanna Government Arts College, IN
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ICTACT Journal on Image and Video Processing, Vol 11, No 1 (2020), Pagination: 2217-2223Abstract
Pancreatic Cancer (PC) may be a leading reason behind death worldwide and its prognosis is extremely poor within the present scenario. There are numerous methods and techniques for tumor identification in brain, breast, lungs, but limited work was done on pancreatic tumor detection. Pancreatic tumor image classification is usually provided by computer-aided screening (CAD), diagnosis and quantitative evaluations in radiology images like CT and MRI. Tumor classification through these methods may help to trace, predict and endorse customized therapy as part of effective treatment, without invasions of cancer. Nowadays, Convolutional Neural Networks (CNN) have shown promising results for precise pancreatic image classification. As a prominent, the algorithms are required to work out and classify the categories of pancreatic tumors at early stages for saving most of the life. Because of the various shapes, huge sample size, processing and analyzing big databases, new statistical methods are to be implemented. On the opposite hand, detection of tumors within the medical images also become difficult since the standard of input images. This paper mainly concentrates on a study of carcinoma and also the recent research on tumor detection and classification in medical images. The convolution neural network (CNN) developed in recent years has been widely utilized in the sector of image processing because it's good at handling image classification and recognition problems and has brought great improvement within the accuracy of the many machine learning tasks. One in every of the foremost powerful approaches to resolve image recognition and classification problem is that the CNN. The experimental results demonstrate that the proposed approach can improve the performance of the classification accuracy.Keywords
CNN, Classification, Deep Learning, Medical Image Analysis, Pancreatic Cancer, Adenocarcinomas.References
- N. Sharma, V. Jain and A. Mishra, “An Analysis of Convolutional Neural Networks for Image Classification”, Procedia Computer Science, Vol. 132, pp. 377-384, 2018.
- K. Fukushima, “Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition”, Neural networks, Vol. 1, No. 2, pp. 119-130, 1988.
- D. Cires, U. Meier, J. Masci and J. Schmidhuber, “Multi-Column Deep Neural Network for Traffic Sign Classification”, Neural Networks, Vol. 32, No. 1, pp. 333-338, 2012.
- F. Ning, D. Delhomme, Y. LeCun, F. Piano, L. Bottou and P.E. Barbano, “Toward Automatic Phenotyping of Developing Embryos from Videos”, IEEE Transactions on Image Processing, Vol. 14, No. 9, pp. 1360-1371, 2005.
- I. Sutskever and G.E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks”, Advances in Neural Information Processing Systems, Vol. 2012, pp. 1097-1105, 2012.
- Laila Marifatul Azizah, Sitti Fadillah Umayah, Slamet Riyadi, Cahya Damarjati and Nafi Ananda Utama “Deep Learning Implementation using Convolutional Neural Network in Mangosteen Surface Defect Detection”, Proceedings of International Conference on Control System, Computing and Engineering, pp. 242-246, 2017.
- Hasbi Ash Shiddieqy, Farkhad Ihsan Hariadi and Trio Adiono “Implementation of Deep-Learning based Image Classification on Single Board Computer”, Proceedings of International Symposium on Electronics and Smart Devices, pp. 133-137, 2017.
- T.M. Lilles and R.W. Kiefer, “Remote Sensing and Image Interpretation”, 5th Edition, Wiley, 2004.
- Yann Lecun, Leon Bottou, Yoshua Bengio and Patrick Haffner, “Gradient based Learning Applied to Document Recognition”, Proceedings of the IEEE, Vol. 86, pp. 2278-2324, 1998.
- H.S. Baird, “Document Image Defect Models”, IEEE Computer Society Press, 1995.
- F.J. Huang, Y. Lecun, “Large-Scale Learning with SVM and Convolutional for Generic Object Categorization”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 284-291, 2006.
- D.C. Ciresan, U. Meier, J. Masci, L. Maria Gambardella and J. Schmidhuber, “Flexible, High Performance Convolutional Neural Networks for Image Classification”, Proceedings of International Conference on Artificial Intelligence, pp. 1237-1242, 2011.
- K. Honda, Y. Hayashida, T. Umaki, T. Okusaka, T. Kosuge, S. Kikuchi and F. Moriyasu, “Possible Detection of Pancreatic Cancer by Plasma Protein Profiling”, Cancer Research, Vol. 65, No. 22, pp. 10613-10622, 2005.
- H.R. Roth, A. Farag, L. Lu, E.B. Turkbey and R.M. Summers, “Deep Convolutional Networks for Pancreas Segmentation in CT Imaging”, Proceedings of International Conference on Optics and Photonics, pp. 1-12, 2015.
- Saftoiu P. Vilmann, F. Gorunescu, D.I. Gheonea, M. Gorunescu, T. Ciurea and S.Iordache, “Neural Network Analysis of Dynamic Sequences of EUS Elastography used for the Differential Diagnosis of Chronic Pancreatitis and Pancreatic Cancer”, Gastrointestinal Endoscopy, Vol. 68, No. 6, pp. 1086-1094, 2008.
- S. Hussein, P. Kandel, C.W. Bolan, M.B. Wallace and U. Bagci, “Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches”, IEEE Transactions on Medical Imaging, Vol. 38, No. 8, pp. 1777-1787, 2019.
- D. Ramsay, M. Marshall, S. Song, M. Zimmerman, S. Edmunds, I. Yusoff and R. Mendelson, “Identification and Staging of Pancreatic Tumors using Computed Tomography, Endoscopic Ultrasound, and Mangafodipir Trisodium-Enhanced Magnetic Resonance Imaging”, Australasian Radiology, Vol. 48, No. 2, pp. 154-161, 2004.
- N.A. Schultz, C. Dehlendorff, B.V. Jensen, J.K. Bjerregaard, K.R. Nielsen, S.E. Bojesen and K.K. Andersen, “Microrna Biomarkers in Whole Blood for the Detection of Pancreatic Cancer”, JAMA, Vol. 311, No. 4, pp. 392-404, 2014.
- J. Behrmann, C. Etmann, T. Boskamp, R. Casadonte and J. Kriegsmann, “Deep Learning for Tumor Classification in Imaging Mass Spectrometry”, Bioinformatics, Vol. 34, No. 7, pp. 1215-1223, 2017.
- Jun E. Liu and Feng Ping An, “Image Classification Algorithm Based on Deep Learning-Kernel Function”, Scientific Programming, Vol. 2020, pp. 1-14, 2020.
- Narender Kumar and Dharmender Kumar, “Classification using Artificial Neural Network Optimized with Bat Algorithm”, International Journal of Innovative Technology and Exploring Engineering, Vol. 9, No. 3, pp. 1-12, 2020.
- Samir S. Yadav and Shivajirao M. Jadhav, “Deep Convolutional Neural Network based Medical Image Classification for Disease Diagnosis”, Big Data, Vol. 6, No. 2, pp. 113-124, 2019.
- L. Xin and Z. Wang, “Research on Image Classification Model based on Deep Convolution Neural Network”, Journal on Image and Video Processing, Vol. 40, No. 3, pp. 1-18, 2019.
- Yanan Sun, Bing Xue, Mengjie Zhang and Gary G. Yen, “Evolving Deep Convolutional Neural Networks for Image Classification”, IEEE Transactions on Evolutionary Computation, Vol. 24, No. 2, pp. 1-19, 2020.
- Yali Peng, Lingjun Li, Shigang Liu, Xili Wang and Jun Li, “Weighted Constraint Based Dictionary Learning for Image Classification”, Pattern Recognition Letters, Vol. 130, pp. 99-106, 2020.
- Somenath Bera and Vimal K. Shrivastava, “Analysis of Various Optimizers on Deep Convolutional Neural Network Model in the Application of Hyperspectral Remote Sensing Image Classification”, International Journal of Remote Sensing, Vol. 41, No. 7, pp. 6355-6383, 2019.
- Agnieszka Mikołajczyk and Michał Grochowski, “Data Augmentation for Improving Deep Learning in Image Classification Problem”, Proceedings of IEEE International Workshop on Interdisciplinary PhD, pp. 222-227, 2018.
- S. Manju and K. Helenprabha, “A Structured Support Vector Machine for Hyperspectral Satellite Image Segmentation and Classification based on Modified Swarm Optimization Approach”, Journal of Ambient Intelligence and Humanized Computing, Vol. 22, No. 1, pp. 1-16, 2019.
- Bin Wang, Yanan Sun, Bing Xue and Mengjie Zhang, “Evolving Deep Convolutional Neural Networks by Variable-Length Particle Swarm Optimization for Image Classification”, Proceedings of IEEE International Congress on Evolutionary Computation, pp. 8-13, 2018.
- Yanan Sun, Bing Xue, Mengjie Zhang, Gary G. Yen and Jiancheng Lv, “Automatically Designing CNN Architectures using the Genetic Algorithm for Image Classification”, IEEE Transactions on Cybernetics, Vol. 50, No. 9, pp. 3840-3854, 2020.
- Benteng Ma, Xiang Li, Yong Xia and Yanning Zhang, “Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification”, Neurocomputing, Vol. 479, pp. 152-161, 2019.
- Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham and Jinman Kim, “Unsupervised Deep Transfer Feature Learning for Medical Image Classification”, Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 8-11, 2019.
- Akif Dogantekin, Fatih Ozyurt, Engin Avci and Mustafa Ko, “A Novel Approach for Liver Image Classification: PH-C-ELM”, Measurement, Vol. 137, pp. 332-338, 2019.
- Yangyang Li, Junjie Xiao, Yanqiao Chen and Licheng Jiao, “Evolving Deep Convolutional Neural Networks by Quantum behaved Particle Swarm Optimization with Binary Encoding for Image Classification”, Neurocomputing, Vol. 362, pp. 156-165, 2019.
- Fatih Ozyurt, Turker Tuncer, Engin Avci, Mustafa Koç and Ihsan Serhatlio Glu, “A Novel Liver Image Classification Method using Perceptual Hash-Based Convolutional Neural Network”, Arabian Journal for Science and Engineering, Vol. 44, pp. 3173-3182, 2019.
- Florian Scheidegger, Roxana Istrate, Giovanni Mariani, Luca Benini, Costas Bekas and Cristiano Malossi, “Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy”, Visual Computers, Vol. 43, pp. 1-17, 2020..