RESEARCH PROGRESS OF IMAGE CLASSIFICATION BASED ON DEEP LEARNING AND DATA DRIVEN
Keywords:
Image Classification, Deep Learning, Data Driven, Overview.Abstract
The research progress of image classification based on deep learning and data-driven is done by many researchers. The main aim of the researcher is to improve the accuracy of the classifier which can be used as a tool in various applications like security, medical, etc. There are many other uses for this technology that are not yet known to us. In this article we will discuss about some methods to improve our results with deep learning and data-driven techniques.Deep learning is a subfield of machine learning that has been used to solve many problems in computer vision and natural language processing. In this paper, we propose an image classification algorithm based on deep convolutional neural network (DCNN) and data-driven feature selection method. The DCNNs are trained with the help of transfer learning from existing state-of-the-art deep networks such as Alexnet, GoogLeNet, CNN, SVM, etc., which have been successfully applied for object detection.References
[1] Sen Jia; Shuguo Jiang; Zhijie Lin; Nanying Li; Meng Xu; Shiqi Yu; "A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples", ARXIV, 2021.
[2] Wilfried W?ber; Lars Mehnen; Peter Sykacek; Harald Meimberg; "Investigating Explanatory Factors of Machine Learning Models for Plant Classification", PLANTS (BASEL, SWITZERLAND), 2021.
[3] Jashanpreet Singh Sraw; Keshav Kumar; "Using Static and Dynamic Malware Features to Perform Malware Ascription", ARXIV, 2021.
[4] Wenwen Li; Pu Chen; Bo Xiong; Guandong Liu; Shuliang Dou; Yaohui Zhan; Zhiyuan Zhu; Yao Li; Wei Ma; "Deep Learning Modeling Strategy for Material Science: from Natural Materials to Metamaterials", JOURNAL OF PHYSICS: MATERIALS, 2022.
[5] Hao Jiang; Yanning Zhou; Yi Lin; Ronald CK Chan; Jiang Liu; Hao Chen; "Deep Learning for Computational Cytology: A Survey", ARXIV, 2022.
[6] Lizy Abraham; Steven Davy; Muhammad Zawish; Rahul Mhapsekar; John A Finn; Patrick Moran; "Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks", SENSORS (BASEL, SWITZERLAND), 2022.
[7] Michael James Horry; Subrata Chakraborty; Biswajeet Pradhan; Nagesh Shukla; Sanjoy Paul; "2-speed Network Ensemble for Efficient Classification of Incremental Land-use/land-cover Satellite Image Chips", ARXIV, 2022.
[8] Zeyu Zhang; Madison Pope; Nadia Shakoor; Robert Pless; Todd C Mockler; Abby Stylianou; "Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum", FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2022.
[9] Lin Wang; Xiufen Ye; Donghao Zhang; Wanji He; Lie Ju; Xin Wang; Wei Feng; Kaimin Song; Xin Zhao; Zongyuan Ge; "3D Matting: A Soft Segmentation Method Applied in Computed Tomography", ARXIV, 2022.
[10] Mingyang Zhang; Ziqi Di; Maoguo Gong; Yue Wu; Hao Li; Xiangming Jiang; "Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification", ARXIV, 2022.
[11] Hoo-Chang Shin; Holger R Roth; Mingchen Gao; Le Lu; Ziyue Xu; Isabella Nogues; Jianhua Yao; Daniel Mollura; Ronald M Summers; "Deep Convolutional Neural Networks For Computer-Aided Detection: CNN Architectures, Dataset Characteristics And Transfer Learning", IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016.
[12] Hang Li; Zhengdong Lu; "Deep Learning For Information Retrieval", SIGIR, 2016.
[13] Gong Cheng; Junwei Han; Xiaoqiang Lu; "Remote Sensing Image Scene Classification: Benchmark and State of The Art", PROCEEDINGS OF THE IEEE, 2017.
[14] Mingyi He; Bo Li; Huahui Chen; "Multi-scale 3D Deep Convolutional Neural Network for Hyperspectral Image Classification", 2017 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2017.
[15] Morgan P McBee; Omer A Awan; Andrew T Colucci; Comeron W Ghobadi; Nadja Kadom; Akash P Kansagra; Srini Tridandapani; William F Auffermann; "Deep Learning In Radiology", ACADEMIC RADIOLOGY, 2018.
[16] Wei Wang; Meihui Zhang; Gang Chen; H. V. Jagadish; Beng Chin Ooi; Kian-Lee Tan; "Database Meets Deep Learning: Challenges And Opportunities", ARXIV, 2019.
[17] David Griffiths; Jan Boehm; "A Review On Deep Learning Techniques For 3D Sensed Data Classification", ARXIV, 2019.
[18] Pronnoy Dutta; Pradumn Upadhyay; Madhurima De; R.G. Khalkar; "Medical Image Analysis Using Deep Convolutional Neural Networks: CNN Architectures and Transfer Learning", 2020 INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT), 2020.
[19] Sunae So; Trevon Badloe; Jaebum Noh; Jorge Bravo-Abad; Junsuk Rho; "Deep Learning Enabled Inverse Design in Nanophotonics", NANOPHOTONICS, 2020.
[20] Chang Zu Chen; Qi Wu; Zuo Yong Li; Lei Xiao; Zhong Yi Hu; "Diagnosis of Alzheimer's Disease Based on Deeply-Fused Nets", COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2020.
[21] Jaime Gallego; Aníbal Pedraza; Samuel Lopez; Georg Steiner; Lucia Gonzalez; Arvydas Laurinavicius; Gloria Bueno; "Glomerulus Classification and Detection Based on Convolutional Neural Networks", J. IMAGING, 2018.
[22] Xinzhuo Zhao; Liyao Liu; Shouliang Qi; Yueyang Teng; Jianhua Li; Wei Qian; "Agile Convolutional Neural Network For Pulmonary Nodule Classification Using CT Images", INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2018.
[23] Erkan Deniz; Abdulkadir ?engür; Zehra Kadiro?lu; Yanhui Guo; Varun Bajaj; ümit Budak; "Transfer Learning Based Histopathologic Image Classification For Breast Cancer Detection", HEALTH INFORMATION SCIENCE AND SYSTEMS, 2018.
[24] Haoming Lin; Yuyang Hu; Siping Chen; Jianhua Yao; Ling Zhang; "Fine-Grained Classification Of Cervical Cells Using Morphological And Appearance Based Convolutional Neural Networks", ARXIV, 2018.
[25] Yang Yang; Lin-Feng Yan; Xin Zhang; Yu Han; Hai-Yan Nan; Yu-Chuan Hu; Bo Hu; Song-Lin Yan; Jin Zhang; Dong-Liang Cheng; Xiang-Wei Ge; Guang-Bin Cui; Di Zhao; Wen Wang; "Glioma Grading On Conventional MR Images: A Deep Learning Study With Transfer Learning", FRONTIERS IN NEUROSCIENCE, 2018.
[26] Ping Ma; Hongli Zhang; Wenhui Fan; Cong Wang; Guangrui Wen; Xining Zhang; "A Novel Bearing Fault Diagnosis Method Based on 2D Image Representation and Transfer Learning-convolutional Neural Network", MEASUREMENT SCIENCE AND TECHNOLOGY, 2019.
[27] Sachin B. Jadhav; "Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification", IAES INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE, 2019.
[28] Prabira Kumar Sethy; Nalini Kanta Barpanda; Amiya Kumar Rath; Santi Kumari Behera; "Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network",