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Nitin, Gupta SB, Yadav R, Bovand F, Tyagi PK. Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests. FRONTIERS IN PLANT SCIENCE 2023; 14:1101943. [PMID: 36895868 PMCID: PMC9989032 DOI: 10.3389/fpls.2023.1101943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/24/2023] [Indexed: 11/07/2023]
Abstract
Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.
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Affiliation(s)
- Nitin
- Department of Computer Science and Engineering, Indira Gandhi University, Meerpur, Rewari, Haryana, India
| | - Satinder Bal Gupta
- Department of Computer Science and Engineering, Indira Gandhi University, Meerpur, Rewari, Haryana, India
| | - RajKumar Yadav
- Department of Computer Science and Engineering, University Institute of Engineering & Technology, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Fatemeh Bovand
- Department of Agronomy and Plant Breeding, Islamic Azad University, Arak, Iran
| | - Pankaj Kumar Tyagi
- Department of Biotechnology, Noida Institute of Engineering and Technology, Greater Noida, India
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Garcea F, Serra A, Lamberti F, Morra L. Data augmentation for medical imaging: A systematic literature review. Comput Biol Med 2023; 152:106391. [PMID: 36549032 DOI: 10.1016/j.compbiomed.2022.106391] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/22/2022] [Accepted: 11/29/2022] [Indexed: 12/13/2022]
Abstract
Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. However, collecting large datasets for medical imaging is still a challenge due to privacy concerns and labeling costs. Data augmentation makes it possible to greatly expand the amount and variety of data available for training without actually collecting new samples. Data augmentation techniques range from simple yet surprisingly effective transformations such as cropping, padding, and flipping, to complex generative models. Depending on the nature of the input and the visual task, different data augmentation strategies are likely to perform differently. For this reason, it is conceivable that medical imaging requires specific augmentation strategies that generate plausible data samples and enable effective regularization of deep neural networks. Data augmentation can also be used to augment specific classes that are underrepresented in the training set, e.g., to generate artificial lesions. The goal of this systematic literature review is to investigate which data augmentation strategies are used in the medical domain and how they affect the performance of clinical tasks such as classification, segmentation, and lesion detection. To this end, a comprehensive analysis of more than 300 articles published in recent years (2018-2022) was conducted. The results highlight the effectiveness of data augmentation across organs, modalities, tasks, and dataset sizes, and suggest potential avenues for future research.
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Affiliation(s)
- Fabio Garcea
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Alessio Serra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Fabrizio Lamberti
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy.
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Nguyen D, Nguyen H, Ong H, Le H, Ha H, Duc NT, Ngo HT. Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer's disease. IBRO Neurosci Rep 2022; 13:255-263. [PMID: 36590098 PMCID: PMC9795286 DOI: 10.1016/j.ibneur.2022.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/07/2022] [Accepted: 08/31/2022] [Indexed: 01/04/2023] Open
Abstract
In recent years, Alzheimer's disease (AD) diagnosis using neuroimaging and deep learning has drawn great research attention. However, due to the scarcity of training neuroimaging data, many deep learning models have suffered from severe overfitting. In this study, we propose an ensemble learning framework that combines deep learning and machine learning. The deep learning model was based on a 3D-ResNet to exploit 3D structural features of neuroimaging data. Meanwhile, Extreme Gradient Boosting (XGBoost) machine learning was applied on a voxel-wise basis to draw the most significant voxel groups out of the image. The 3D-ResNet and XGBoost predictions were combined with patient demographics and cognitive test scores (Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR)) to give a final diagnosis prediction. Our proposed method was trained and validated on brain MRI brain images of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. During the training phase, multiple data augmentation methods were employed to tackle overfitting. Our test set contained only baseline scans, i.e., the first visit scans since we aimed to investigate the ability of our approach in detecting AD during the first visit of AD patients. Our 5-fold cross-validation implementation achieved an average AUC of 100% during training and 96% during testing. Using the same computer, our method was much faster in scoring a prediction, approximately 10 min, than feature extraction-based machine learning methods, which often take many hours to score a prediction. To make the prediction explainable, we visualized the brain MRI image regions that primarily affected the 3D-ResNet model's prediction via heatmap. Lastly, we observed that proper generation of test sets was critical to avoiding the data leakage issue and ensuring the validity of results.
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Affiliation(s)
- Dong Nguyen
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Hoang Nguyen
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Hong Ong
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam
| | - Hoang Le
- Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada
| | - Huong Ha
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam,Corresponding authors at: School of Biomedical Engineering, International University, Vietnam.
| | - Nguyen Thanh Duc
- Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea,Montreal Neurological Institute, McGill University, Montreal, Canada,Corresponding author at: Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea.
| | - Hoan Thanh Ngo
- School of Biomedical Engineering, International University, Vietnam,Vietnam National University, Ho Chi Minh City, Vietnam,Corresponding authors at: School of Biomedical Engineering, International University, Vietnam.
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Oza P, Sharma P, Patel S, Adedoyin F, Bruno A. Image Augmentation Techniques for Mammogram Analysis. J Imaging 2022; 8:jimaging8050141. [PMID: 35621905 PMCID: PMC9147240 DOI: 10.3390/jimaging8050141] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 01/30/2023] Open
Abstract
Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.
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Affiliation(s)
- Parita Oza
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
- Correspondence: or (P.O.); (A.B.)
| | - Paawan Sharma
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Samir Patel
- Computer Science and Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India; (P.S.); (S.P.)
| | - Festus Adedoyin
- Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK;
| | - Alessandro Bruno
- Department of Computing and Informatics, Bournemouth University, Poole BH12 5BB, UK;
- Correspondence: or (P.O.); (A.B.)
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