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Chempak Kumar A, Mubarak DMN. Ensembled CNN with artificial bee colony optimization method for esophageal cancer stage classification using SVM classifier. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:31-51. [PMID: 37980593 DOI: 10.3233/xst-230111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
BACKGROUND Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians. OBJECTIVE To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages. METHODS The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance. RESULTS The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method. CONCLUSION This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification.
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Affiliation(s)
- A Chempak Kumar
- Department of Computer Science, University of Kerala, Trivandrum, Kerala, India
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Chou CK, Nguyen HT, Wang YK, Chen TH, Wu IC, Huang CW, Wang HC. Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning. Cancers (Basel) 2023; 15:3783. [PMID: 37568599 PMCID: PMC10417640 DOI: 10.3390/cancers15153783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/17/2023] [Accepted: 07/23/2023] [Indexed: 08/13/2023] Open
Abstract
Early detection of esophageal cancer through endoscopic imaging is pivotal for effective treatment. However, the intricacies of endoscopic diagnosis, contingent on the physician's expertise, pose challenges. Esophageal cancer features often manifest ambiguously, leading to potential confusions with other inflammatory esophageal conditions, thereby complicating diagnostic accuracy. In recent times, computer-aided diagnosis has emerged as a promising solution in medical imaging, particularly within the domain of endoscopy. Nonetheless, contemporary AI-based diagnostic models heavily rely on voluminous data sources, limiting their applicability, especially in scenarios with scarce datasets. To address this limitation, our study introduces novel data training strategies based on transfer learning, tailored to optimize performance with limited data. Additionally, we propose a hybrid model integrating EfficientNet and Vision Transformer networks to enhance prediction accuracy. Conducting rigorous evaluations on a carefully curated dataset comprising 1002 endoscopic images (comprising 650 white-light images and 352 narrow-band images), our model achieved exceptional outcomes. Our combined model achieved an accuracy of 96.32%, precision of 96.44%, recall of 95.70%, and f1-score of 96.04%, surpassing state-of-the-art models and individual components, substantiating its potential for precise medical image classification. The AI-based medical image prediction platform presents several advantageous characteristics, encompassing superior prediction accuracy, a compact model size, and adaptability to low-data scenarios. This research heralds a significant stride in the advancement of computer-aided endoscopic imaging for improved esophageal cancer diagnosis.
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Affiliation(s)
- Chu-Kuang Chou
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan;
- Obesity Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan
| | - Hong-Thai Nguyen
- Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan;
| | - Yao-Kuang Wang
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan;
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan;
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan
| | - Tsung-Hsien Chen
- Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 60002, Taiwan;
| | - I-Chen Wu
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan;
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung City 80756, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan;
- Hitspectra Intelligent Technology Co., Ltd., Kaohsiung City 80661, Taiwan
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Hosseini F, Asadi F, Emami H, Ebnali M. Machine learning applications for early detection of esophageal cancer: a systematic review. BMC Med Inform Decis Mak 2023; 23:124. [PMID: 37460991 DOI: 10.1186/s12911-023-02235-y] [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: 04/04/2023] [Accepted: 07/12/2023] [Indexed: 07/20/2023] Open
Abstract
INTRODUCTION Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. METHODS We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review. RESULTS The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. CONCLUSION Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.
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Affiliation(s)
- Farhang Hosseini
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hassan Emami
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahdi Ebnali
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
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Mukhtorov D, Rakhmonova M, Muksimova S, Cho YI. Endoscopic Image Classification Based on Explainable Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:3176. [PMID: 36991887 PMCID: PMC10058443 DOI: 10.3390/s23063176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Deep learning has achieved remarkably positive results and impacts on medical diagnostics in recent years. Due to its use in several proposals, deep learning has reached sufficient accuracy to implement; however, the algorithms are black boxes that are hard to understand, and model decisions are often made without reason or explanation. To reduce this gap, explainable artificial intelligence (XAI) offers a huge opportunity to receive informed decision support from deep learning models and opens the black box of the method. We conducted an explainable deep learning method based on ResNet152 combined with Grad-CAM for endoscopy image classification. We used an open-source KVASIR dataset that consisted of a total of 8000 wireless capsule images. The heat map of the classification results and an efficient augmentation method achieved a high positive result with 98.28% training and 93.46% validation accuracy in terms of medical image classification.
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Tang S, Yu X, Cheang CF, Liang Y, Zhao P, Yu HH, Choi IC. Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images. Comput Biol Med 2023; 157:106723. [PMID: 36907035 DOI: 10.1016/j.compbiomed.2023.106723] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/04/2023] [Accepted: 02/26/2023] [Indexed: 03/07/2023]
Abstract
Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases using classification and segmentation, models based on convolutional neural network (CNN) have difficulties in distinguishing the similarities among some ambiguous types of lesions presented in endoscopic images, and in the training when lacking labeled datasets. Those will prevent CNN from further improving the accuracy of diagnosis. To address these challenges, we first proposed a Multi-task Network (TransMT-Net) capable of simultaneously learning two tasks (classification and segmentation), which has the transformer designed to learn global features and can combine the advantages of CNN in learning local features so that to achieve a more accurate prediction in identifying the lesion types and regions in GI tract endoscopic images. We further adopted the active learning in TransMT-Net to tackle the labeled image-hungry problem. A dataset was created from the CVC-ClinicDB dataset, Macau Kiang Wu Hospital, and Zhongshan Hospital to evaluate the model performance. Then, the experimental results show that our model not only achieved 96.94% accuracy in the classification task and 77.76% Dice Similarity Coefficient in the segmentation task but also outperformed those of other models on our test set. Meanwhile, active learning also produced positive results for the performance of our model with a small-scale initial training set, and even its performance with 30% of the initial training set was comparable to that of most comparable models with the full training set. Consequently, the proposed TransMT-Net has demonstrated its potential performance in GI tract endoscopic images and it through active learning can alleviate the shortage of labeled images.
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Affiliation(s)
- Suigu Tang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Xiaoyuan Yu
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Chak Fong Cheang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China.
| | - Yanyan Liang
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Penghui Zhao
- Faculty of Innovation Engineering-School of Computer Science and Engineering, Macau University of Science and Technology, Macao Special Administrative Region of China
| | - Hon Ho Yu
- Kiang Wu Hospital, Macao Special Administrative Region of China
| | - I Cheong Choi
- Kiang Wu Hospital, Macao Special Administrative Region of China
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Xu Q, Xu QQ, Shi N, Dong LN, Zhu H, Xu K. A multitask classification framework based on vision transformer for predicting molecular expressions of glioma. Eur J Radiol 2022; 157:110560. [DOI: 10.1016/j.ejrad.2022.110560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/29/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2022]
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Alharbe NR, Munshi RM, Khayyat MM, Khayyat MM, Abdalaha Hamza SH, Aljohani AA. Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4629178. [PMID: 36156959 PMCID: PMC9507698 DOI: 10.1155/2022/4629178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/13/2022] [Accepted: 08/10/2022] [Indexed: 11/20/2022]
Abstract
Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate. Automated recognition of EC can aid physicians in improving diagnostic performance and accuracy. However, the classification of EC is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness. The recent developments of deep learning (DL) and computer-aided diagnosis (CAD) models have been useful for designing accurate EC classification models. In this aspect, this study develops an atom search optimization with a deep transfer learning-driven EC classification (ASODTL-ECC) model. The presented ASODTL-ECC model mainly examines the medical images for the existence of EC in a timely and accurate manner. To do so, the presented ASODTL-ECC model employs Gaussian filtering (GF) as a preprocessing stage to enhance image quality. In addition, the deep convolution neural network- (DCNN-) based residual network (ResNet) model is applied as a feature extraction approach. Besides, ASO with an extreme learning machine (ELM) model is utilized for identifying the presence of EC, showing the novelty of the work. The performance of the ASODTL-ECC model is assessed and compared with existing models under several medical images. The experimental results pointed out the improved performance of the ASODTL-ECC model over recent approaches.
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Affiliation(s)
| | - Raafat M. Munshi
- Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Manal M. Khayyat
- Department of Information Systems, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Mashael M. Khayyat
- Department of Information Systems and Technology, Faculty of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Saadia Hassan Abdalaha Hamza
- Department of Computer Science College of Science and Humanities in Al-Sulail, Prince Sattam Bin Abdulaziz University, Saudi Arabia
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Zhao Q, Zheng C, Ma W. An Improved Crucible Spatial Bubble Detection Based on YOLOv5 Fusion Target Tracking. SENSORS (BASEL, SWITZERLAND) 2022; 22:6356. [PMID: 36080814 PMCID: PMC9460891 DOI: 10.3390/s22176356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
A three-dimensional spatial bubble counting method is proposed to solve the problem of the existing crucible bubble detection only being able to perform two-dimensional statistics. First, spatial video images of the transparent layer of the crucible are acquired by a digital microscope, and a quartz crucible bubble dataset is constructed independently. Secondly, to address the problems of poor real-time and the insufficient small-target detection capability of existing methods for quartz crucible bubble detection, rich detailed feature information is retained by reducing the depth of down-sampling in the YOLOv5 network structure. In the neck, the dilated convolution algorithm is used to increase the feature map perceptual field to achieve the extraction of global semantic features; in front of the detection layer, an effective channel attention network (ECA-Net) mechanism is added to improve the capability of expressing significant channel characteristics. Furthermore, a tracking algorithm based on Kalman filtering and Hungarian matching is presented for bubble counting in crucible space. The experimental results demonstrate that the detector algorithm presented in this paper can effectively reduce the missed detection rate of tiny bubbles and increase the average detection precision from 96.27% to 98.76% while reducing weight by half and reaching a speed of 82 FPS. The excellent detector performance improves the tracker's accuracy significantly, allowing for real-time and high-precision counting of bubbles in quartz crucibles. It is an effective method for detecting crucible spatial bubbles.
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Affiliation(s)
- Qian Zhao
- School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Chao Zheng
- School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Wenyue Ma
- School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
- Xi’an Dishan Vision Technology Limited Company, Xi’an 712044, China
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Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification. SENSORS 2022; 22:s22072801. [PMID: 35408415 PMCID: PMC9003289 DOI: 10.3390/s22072801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/26/2022] [Accepted: 04/02/2022] [Indexed: 01/10/2023]
Abstract
Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.
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Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9523009. [PMID: 35320996 PMCID: PMC8938070 DOI: 10.1155/2022/9523009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 01/13/2023]
Abstract
As multimedia technology is developing and growing these days, the use of an enormous number of images and its datasets is likewise expanding at a quick rate. Such datasets can be utilized for the purpose of image retrieval. This research focuses on extraction of similar images established on its different features for the image retrieval purpose from huge dataset of images. In this paper initially, the query image is searched within the available dataset and, then, the color difference histogram (CDH) descriptor is employed to retrieve the images from database. The basic characteristic of CDH is that it counts the color difference stuck among two distinct labels in the L∗a∗b∗ color space. This method is experimented on random images used for various medical purposes. Various unlike features of an image are extracted via different distance methods. The precision rate, recall rate, and F-measure are all used to evaluate the system's performance. Comparative analysis in terms of F-measure is also made to check for the best distance method used for retrieval of images.
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