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Bera A, Bhattacharjee D, Krejcar O. PND-Net: plant nutrition deficiency and disease classification using graph convolutional network. Sci Rep 2024; 14:15537. [PMID: 38969738 DOI: 10.1038/s41598-024-66543-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/02/2024] [Indexed: 07/07/2024] Open
Abstract
Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40 × : 95.50%, and BreakHis 100 × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net's aptness for agricultural growth as well as human cancer classification.
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Affiliation(s)
- Asish Bera
- Department of Computer Science and Information Systems, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India.
| | - Debotosh Bhattacharjee
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, 700032, India
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Ondrej Krejcar
- Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic
- Skoda Auto University, Na Karmeli 1457, 293 01, Mlada Boleslav, Czech Republic
- Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
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Deep learning for computational cytology: A survey. Med Image Anal 2023; 84:102691. [PMID: 36455333 DOI: 10.1016/j.media.2022.102691] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/22/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022]
Abstract
Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) approaches have made significant achievements in medical image analysis, leading to boosting publications of cytological studies. In this article, we survey more than 120 publications of DL-based cytology image analysis to investigate the advanced methods and comprehensive applications. We first introduce various deep learning schemes, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize public datasets, evaluation metrics, versatile cytology image analysis applications including cell classification, slide-level cancer screening, nuclei or cell detection and segmentation. Finally, we discuss current challenges and potential research directions of computational cytology.
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Xu C, Li M, Li G, Zhang Y, Sun C, Bai N. Cervical Cell/Clumps Detection in Cytology Images Using Transfer Learning. Diagnostics (Basel) 2022; 12:diagnostics12102477. [PMID: 36292166 PMCID: PMC9600700 DOI: 10.3390/diagnostics12102477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 12/04/2022] Open
Abstract
Cervical cancer is one of the most common and deadliest cancers among women and poses a serious health risk. Automated screening and diagnosis of cervical cancer will help improve the accuracy of cervical cell screening. In recent years, there have been many studies conducted using deep learning methods for automatic cervical cancer screening and diagnosis. Deep-learning-based Convolutional Neural Network (CNN) models require large amounts of data for training, but large cervical cell datasets with annotations are difficult to obtain. Some studies have used transfer learning approaches to handle this problem. However, such studies used the same transfer learning method that is the backbone network initialization by the ImageNet pre-trained model in two different types of tasks, the detection and classification of cervical cell/clumps. Considering the differences between detection and classification tasks, this study proposes the use of COCO pre-trained models when using deep learning methods for cervical cell/clumps detection tasks to better handle limited data set problem at training time. To further improve the model detection performance, based on transfer learning, we conducted multi-scale training according to the actual situation of the dataset. Considering the effect of bounding box loss on the precision of cervical cell/clumps detection, we analyzed the effects of different bounding box losses on the detection performance of the model and demonstrated that using a loss function consistent with the type of pre-trained model can help improve the model performance. We analyzed the effect of mean and std of different datasets on the performance of the model. It was demonstrated that the detection performance was optimal when using the mean and std of the cervical cell dataset used in the current study. Ultimately, based on backbone Resnet50, the mean Average Precision (mAP) of the network model is 61.6% and Average Recall (AR) is 87.7%. Compared to the current values of 48.8% and 64.0% in the used dataset, the model detection performance is significantly improved by 12.8% and 23.7%, respectively.
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Affiliation(s)
- Chuanyun Xu
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China
- College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
| | - Mengwei Li
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China
- Correspondence: (M.L.); (G.L.)
| | - Gang Li
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China
- Correspondence: (M.L.); (G.L.)
| | - Yang Zhang
- College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
| | - Chengjie Sun
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China
| | - Nanlan Bai
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, China
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Zak J, Grzeszczyk MK, Pater A, Roszkowiak L, Siemion K, Korzynska A. Cell image augmentation for classification task using GANs on Pap smear dataset. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Heo J, Lim JH, Lee HR, Jang JY, Shin YS, Kim D, Lim JY, Park YM, Koh YW, Ahn SH, Chung EJ, Lee DY, Seok J, Kim CH. Deep learning model for tongue cancer diagnosis using endoscopic images. Sci Rep 2022; 12:6281. [PMID: 35428854 PMCID: PMC9012779 DOI: 10.1038/s41598-022-10287-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 03/29/2022] [Indexed: 12/29/2022] Open
Abstract
In this study, we developed a deep learning model to identify patients with tongue cancer based on a validated dataset comprising oral endoscopic images. We retrospectively constructed a dataset of 12,400 verified endoscopic images from five university hospitals in South Korea, collected between 2010 and 2020 with the participation of otolaryngologists. To calculate the probability of malignancy using various convolutional neural network (CNN) architectures, several deep learning models were developed. Of the 12,400 total images, 5576 images related to the tongue were extracted. The CNN models showed a mean area under the receiver operating characteristic curve (AUROC) of 0.845 and a mean area under the precision-recall curve (AUPRC) of 0.892. The results indicate that the best model was DenseNet169 (AUROC 0.895 and AUPRC 0.918). The deep learning model, general physicians, and oncology specialists had sensitivities of 81.1%, 77.3%, and 91.7%; specificities of 86.8%, 75.0%, and 90.9%; and accuracies of 84.7%, 75.9%, and 91.2%, respectively. Meanwhile, fair agreement between the oncologist and the developed model was shown for cancer diagnosis (kappa value = 0.685). The deep learning model developed based on the verified endoscopic image dataset showed acceptable performance in tongue cancer diagnosis.
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Affiliation(s)
- Jaesung Heo
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - June Hyuck Lim
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Hye Ran Lee
- Department of Otolaryngology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea
| | - Jeon Yeob Jang
- Department of Otolaryngology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea
| | - Yoo Seob Shin
- Department of Otolaryngology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea
| | - Dahee Kim
- Department of Otorhinolaryngology, Yonsei University, Seoul, Republic of Korea
| | - Jae Yol Lim
- Department of Otorhinolaryngology, Yonsei University, Seoul, Republic of Korea
| | - Young Min Park
- Department of Otorhinolaryngology, Yonsei University, Seoul, Republic of Korea
| | - Yoon Woo Koh
- Department of Otorhinolaryngology, Yonsei University, Seoul, Republic of Korea
| | - Soon-Hyun Ahn
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eun-Jae Chung
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Doh Young Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jungirl Seok
- Department of Otorhinolaryngology-Head & Neck Surgery, National Cancer Center, Goyang, Republic of Korea
| | - Chul-Ho Kim
- Department of Otolaryngology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.
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