1
|
Wu H, Min W, Gai D, Huang Z, Geng Y, Wang Q, Chen R. HD-Former: A hierarchical dependency Transformer for medical image segmentation. Comput Biol Med 2024; 178:108671. [PMID: 38870721 DOI: 10.1016/j.compbiomed.2024.108671] [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: 10/20/2023] [Revised: 04/20/2024] [Accepted: 05/26/2024] [Indexed: 06/15/2024]
Abstract
Medical image segmentation is a compelling fundamental problem and an important auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a valuable tool for addressing the limitations of convolutional neural networks by effectively capturing global relationships and numerous hybrid architectures combining convolutional neural networks (CNNs) and Transformer have been devised to enhance segmentation performance. However, they suffer from multilevel semantic feature gaps and fail to account for multilevel dependencies between space and channel. In this paper, we propose a hierarchical dependency Transformer for medical image segmentation, named HD-Former. First, we utilize a Compressed Bottleneck (CB) module to enrich shallow features and localize the target region. We then introduce the Dual Cross Attention Transformer (DCAT) module to fuse multilevel features and bridge the feature gap. In addition, we design the broad exploration network (BEN) that cascades convolution and self-attention from different percepts to capture hierarchical dense contextual semantic features locally and globally. Finally, we exploit uncertain multitask edge loss to adaptively map predictions to a consistent feature space, which can optimize segmentation edges. The extensive experiments conducted on medical image segmentation from ISIC, LiTS, Kvasir-SEG, and CVC-ClinicDB datasets demonstrate that our HD-Former surpasses the state-of-the-art methods in terms of both subjective visual performance and objective evaluation. Code: https://github.com/barcelonacontrol/HD-Former.
Collapse
Affiliation(s)
- Haifan Wu
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
| | - Weidong Min
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China; Institute of Metaverse, Nanchang University, Nanchang, 330031, China; Jiangxi Key Laboratory of Virtual Reality, Nanchang, 330031, China.
| | - Di Gai
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China; Institute of Metaverse, Nanchang University, Nanchang, 330031, China; Jiangxi Key Laboratory of Virtual Reality, Nanchang, 330031, China.
| | - Zheng Huang
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China.
| | - Yuhan Geng
- School of Public Health, University of Michigan, Ann Arbor, MI, 48105, USA.
| | - Qi Wang
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China; Institute of Metaverse, Nanchang University, Nanchang, 330031, China; Jiangxi Key Laboratory of Virtual Reality, Nanchang, 330031, China.
| | - Ruibin Chen
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China; Information Department, The First Affiliated Hospital of Nanchang University, Nanchang, 330096, China.
| |
Collapse
|
2
|
Wen Y, Wu W, Liufu Y, Pan X, Zhang Y, Qi S, Guan Y. Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model. BMC Cancer 2024; 24:875. [PMID: 39039511 PMCID: PMC11265160 DOI: 10.1186/s12885-024-12611-0] [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: 12/16/2023] [Accepted: 07/08/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis. METHODS 420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists' diagnoses. RESULTS The DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS. CONCLUSIONS A CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.
Collapse
Affiliation(s)
- Yanhua Wen
- Department of Medical Imaging, the Fifth Affiliated Hospital of Guangzhou Medical University, 621 Gangwan Road, Guangzhou, 510700, Guangdong, China
| | - Wensheng Wu
- Department of Medical Imaging, the Fifth Affiliated Hospital of Guangzhou Medical University, 621 Gangwan Road, Guangzhou, 510700, Guangdong, China
| | - Yuling Liufu
- Department of Medical Imaging, the Fifth Affiliated Hospital of Guangzhou Medical University, 621 Gangwan Road, Guangzhou, 510700, Guangdong, China
| | - Xiaohuan Pan
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yingying Zhang
- Department of Medical Imaging, the Fifth Affiliated Hospital of Guangzhou Medical University, 621 Gangwan Road, Guangzhou, 510700, Guangdong, China
| | - Shouliang Qi
- Key Laboratory of Intelligent Computing in Medical Image, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Yubao Guan
- Department of Medical Imaging, the Fifth Affiliated Hospital of Guangzhou Medical University, 621 Gangwan Road, Guangzhou, 510700, Guangdong, China.
| |
Collapse
|
3
|
Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani S. Semantically redundant training data removal and deep model classification performance: A study with chest X-rays. Comput Med Imaging Graph 2024; 115:102379. [PMID: 38608333 PMCID: PMC11144082 DOI: 10.1016/j.compmedimag.2024.102379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024]
Abstract
Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Also, the common use of augmentation methods to generate variety in DL training could limit performance when indiscriminately applied to such data. We hypothesize that semantic redundancy would therefore tend to lower performance and limit generalizability to unseen data and question its impact on classifier performance even with large data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data and demonstrate using the publicly available NIH chest X-ray dataset that the model trained on the resulting informative subset of training data significantly outperforms the model trained on the full training set, during both internal (recall: 0.7164 vs 0.6597, p<0.05) and external testing (recall: 0.3185 vs 0.2589, p<0.05). Our findings emphasize the importance of information-oriented training sample selection as opposed to the conventional practice of using all available training data.
Collapse
Affiliation(s)
| | - Ghada Zamzmi
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Feng Yang
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Zhaohui Liang
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Zhiyun Xue
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Sameer Antani
- National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
| |
Collapse
|
4
|
Sweetline BC, Vijayakumaran C, Samydurai A. Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:988-1007. [PMID: 38347393 PMCID: PMC11169448 DOI: 10.1007/s10278-024-01004-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/06/2023] [Accepted: 12/22/2023] [Indexed: 06/13/2024]
Abstract
Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung, which are either cancerous or non-cancerous. Accurate segmentation of these nodules is crucial for early detection and diagnosis of lung cancer. However, lung nodules can have various shapes, sizes, and densities, making their accurate segmentation a difficult task. Moreover, they can be easily confused with other structures in the lung, including blood vessels and airways, further complicating the segmentation process. To address this challenge, this paper proposes a novel multi-crop convolutional neural network (multi-crop CNN) model that utilizes different sized cropped regions of CT scan images for accurate segmentation of lung nodules. The model consists of three modules, namely the feature representation module, boundary refinement module, and segmentation module. The feature representation module captures features from the lung CT scan image using cropped regions of different sizes, while the boundary refinement module combines the boundary maps and feature maps to generate a final feature map for the segmentation process. The segmentation module produces a high-resolution segmentation map that shows improved accuracy in segmenting cancerous lung nodules. The proposed multi-crop CNN model is evaluated on two segmentation datasets namely LUNA 16 and LIDC-IDRI with an accuracy of 98.3% and 98.5%, respectively. The performances are measured in terms of accuracy, recall, precision, dice coefficient, specificity, AUC/ROC, Hausdorff distance, Jaccard index, and average Hausdorff. Overall, the proposed multi-crop CNN model demonstrates the potential to enhance the lung nodule segmentation accuracy, which could lead to earlier detection and diagnosis of lung cancer and ultimately reduce mortality rates associated with the disease.
Collapse
Affiliation(s)
- B Christina Sweetline
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India.
| | - C Vijayakumaran
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India
| | - A Samydurai
- Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, India
| |
Collapse
|
5
|
Li Y, Liu H, Lv Q, Long J. Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method. BMC Pulm Med 2024; 24:205. [PMID: 38664747 PMCID: PMC11046959 DOI: 10.1186/s12890-024-02987-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces challenges such as difficulty in obtaining specimens and low detection rates. In the clinical diagnosis process, it is necessary to combine clinical symptoms, serological test results, chest Computed tomography (CT) images, molecular biology techniques, and metagenomics next-generation sequencing (mNGS) for comprehensive analysis. PURPOSE This study aims to overcome the limitations of traditional PJP diagnosis methods and develop a non-invasive, efficient, and accurate diagnostic approach for PJP. By using this method, patients can receive early diagnosis and treatment, effectively improving their prognosis. METHODS We constructed an intelligent diagnostic model for PJP based on the different Convolutional Neural Networks. Firstly, we used the Convolutional Neural Network to extract CT image features from patients. Then, we fused the CT image features with clinical information features using a feature fusion function. Finally, the fused features were input into the classification network to obtain the patient's diagnosis result. RESULTS In this study, for the diagnosis of PJP, the accuracy of the traditional PCR diagnostic method is 77.58%, while the mean accuracy of the optimal diagnostic model based on convolutional neural networks is 88.90%. CONCLUSION The accuracy of the diagnostic method proposed in this paper is 11.32% higher than that of the traditional PCR diagnostic method. The method proposed in this paper is an efficient, accurate, and non-invasive early diagnosis approach for PJP.
Collapse
Affiliation(s)
- Yingying Li
- Department of Clinical Laboratory, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hailin Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Qingwen Lv
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| | - Jun Long
- Department of Clinical Laboratory, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| |
Collapse
|
6
|
Chang HH, Wu CZ, Gallogly AH. Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:347-362. [PMID: 38343233 DOI: 10.1007/s10278-023-00928-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/13/2023] [Accepted: 09/25/2023] [Indexed: 03/02/2024]
Abstract
Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement. This study is dedicated to investigating a new CAD scheme for predicting the malignant likelihood of lung nodules in computed tomography (CT) images in light of a deep learning strategy. Conceived from the residual learning and selective kernel, we investigated an efficient residual selective kernel (RSK) block to handle the diversity of lung nodules with various shapes and obscure structures. Founded on this RSK block, we established a multiview RSK network (MRSKNet), to which three anatomical planes in the axial, coronal, and sagittal directions were fed. To reinforce the classification efficiency, seven handcrafted texture features with a filter-like computation strategy were explored, among which the homogeneity (HOM) feature maps are combined with the corresponding intensity CT images for concatenation input, leading to an improved network architecture. Evaluated on the public benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) challenge database with ten-fold cross validation of binary classification, our experimental results indicated high area under receiver operating characteristic (AUC) and accuracy scores. A better compromise between recall and specificity was struck using the suggested concatenation strategy comparing to many state-of-the-art approaches. The proposed pulmonary nodule classification framework exhibited great efficacy and achieved a higher AUC of 0.9711. The association of handcrafted texture features with deep learning models is promising in advancing the classification performance. The developed pulmonary nodule CAD network architecture is of potential in facilitating the diagnosis of lung cancer for further image processing applications.
Collapse
Affiliation(s)
- Herng-Hua Chang
- Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan.
| | - Cheng-Zhe Wu
- Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan
| | - Audrey Haihong Gallogly
- Department of Radiation Oncology, Keck Medical School, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
7
|
Ahmad Z, Malik AK, Qamar N, Islam SU. Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images. Diagnostics (Basel) 2023; 13:3462. [PMID: 37998598 PMCID: PMC10669971 DOI: 10.3390/diagnostics13223462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.
Collapse
Affiliation(s)
- Zeeshan Ahmad
- Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan;
| | - Ahmad Kamran Malik
- Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan;
| | - Nafees Qamar
- School of Health and Behavioral Sciences, Bryant University, Smithfield, RI 02917, USA;
| | - Saif ul Islam
- Department of Computer Science, Institute of Space Technology, Islamabad 44000, Pakistan
| |
Collapse
|
8
|
Ahoor A, Arif F, Sajid MZ, Qureshi I, Abbas F, Jabbar S, Abbas Q. MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model. Diagnostics (Basel) 2023; 13:3195. [PMID: 37892016 PMCID: PMC10606171 DOI: 10.3390/diagnostics13203195] [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: 08/31/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/29/2023] Open
Abstract
The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset's unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system's improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings.
Collapse
Affiliation(s)
- Ayesha Ahoor
- Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan; (A.A.); (F.A.); (M.Z.S.)
| | - Fahim Arif
- Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan; (A.A.); (F.A.); (M.Z.S.)
| | - Muhammad Zaheer Sajid
- Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan; (A.A.); (F.A.); (M.Z.S.)
| | - Imran Qureshi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.J.); (Q.A.)
| | - Fakhar Abbas
- Centre for Trusted Internet and Community, National University of Singapore (NUS), Singapore 119228, Singapore;
| | - Sohail Jabbar
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.J.); (Q.A.)
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.J.); (Q.A.)
| |
Collapse
|
9
|
Liu W, Delalibera Rodrigues R, Yan J, Zhu YT, de Freitas Pereira EJ, Li G, Zheng Q, Zhao L. Complex network-based classification of radiographic images for COVID-19 diagnosis. PLoS One 2023; 18:e0290968. [PMID: 37656697 PMCID: PMC10473542 DOI: 10.1371/journal.pone.0290968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 08/03/2023] [Indexed: 09/03/2023] Open
Abstract
In this work, we present a network-based technique for chest X-ray image classification to help the diagnosis and prognosis of patients with COVID-19. From visual inspection, we perceive that healthy and COVID-19 chest radiographic images present different levels of geometric complexity. Therefore, we apply fractal dimension and quadtree as feature extractors to characterize such differences. Moreover, real-world datasets often present complex patterns, which are hardly handled by only the physical features of the data (such as similarity, distance, or distribution). This issue is addressed by complex networks, which are suitable tools for characterizing data patterns and capturing spatial, topological, and functional relationships in data. Specifically, we propose a new approach combining complexity measures and complex networks to provide a modified high-level classification technique to be applied to COVID-19 chest radiographic image classification. The computational results on the Kaggle COVID-19 Radiography Database show that the proposed method can obtain high classification accuracy on X-ray images, being competitive with state-of-the-art classification techniques. Lastly, a set of network measures is evaluated according to their potential in distinguishing the network classes, which resulted in the choice of communicability measure. We expect that the present work will make significant contributions to machine learning at the semantic level and to combat COVID-19.
Collapse
Affiliation(s)
- Weiguang Liu
- School of Computer Science, Zhongyuan University of Technology, ZhengZhou, China
| | | | - Jianglong Yan
- Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), São Carlos, Brazil
| | - Yu-tao Zhu
- China Branch of BRICS Institute of Future Networks, ShenZhen, China
| | | | - Gen Li
- Henan Key Laboratory on Public Opinion Intelligent Analysis, Zhongyuan University of Technology, ZhengZhou, China
| | - Qiusheng Zheng
- Henan Key Laboratory on Public Opinion Intelligent Analysis, Zhongyuan University of Technology, ZhengZhou, China
| | - Liang Zhao
- China Branch of BRICS Institute of Future Networks, ShenZhen, China
- Department of Computing and Mathematics, FFCLRP, University of São Paulo (USP), Ribeirão Preto, Brazil
| |
Collapse
|
10
|
Zhang Y, Feng W, Wu Z, Li W, Tao L, Liu X, Zhang F, Gao Y, Huang J, Guo X. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1088. [PMID: 37374292 DOI: 10.3390/medicina59061088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.
Collapse
Affiliation(s)
- Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Yan Gao
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| |
Collapse
|
11
|
Al-Azzwi ZHN, Nazarov A. Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods. Asian Pac J Cancer Prev 2023; 24:2141-2148. [PMID: 37378946 PMCID: PMC10505861 DOI: 10.31557/apjcp.2023.24.6.2141] [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: 04/01/2023] [Accepted: 06/21/2023] [Indexed: 06/29/2023] Open
Abstract
OBJECTIVE Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying and classifying brain tumors. For the diagnosis and treatment of cancer diseases, prediction and classification accuracy are crucial. The aim of this study was to improve ensemble deep learning models for classifing brain tumor and increase the performance of structure models by combining different model of deep learning to develop a model with more accurate predictions than the individual models. METHODS Convolutional neural networks (CNNs), which are made up of a single algorithm called CNN model, are the foundation of most current methods for classifying cancer illness images. The model CNN is combined with other models to create other methods of classification called ensemble method. However, compared to a single machine learning algorithm, ensemble machine learning models are more accurate. This study used stacked ensemble deep learning technology. The data set used in this study was obtained from Kaggle and included two categories: abnormal & normal brains. The data set was trained with three models: VGG19, Inception v3, and Resnet 10. RESULT The 96.6% accuracy for binary classification (0,1) have been achieved by stacked ensemble deep learning model with Loss binary cross entropy, and Adam optimizer take into consideration with stacking models. CONCLUSION The stacked ensemble deep learning model can be improved over a single framework.
Collapse
Affiliation(s)
- Zobeda Hatif Naji Al-Azzwi
- School of Radio Engineering and Computer Technology, Moscow Institute of Physics and Technology, Moscow, Russian Federation.
| | - A.N Nazarov
- Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russian Federation.
| |
Collapse
|
12
|
Zhang X, Dong X, Saripan MIB, Du D, Wu Y, Wang Z, Cao Z, Wen D, Liu Y, Marhaban MH. Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer. Thorac Cancer 2023. [PMID: 37183577 DOI: 10.1111/1759-7714.14924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/21/2023] [Accepted: 04/22/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information. METHODS Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics. RESULTS The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models. CONCLUSION The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.
Collapse
Affiliation(s)
- Xiaolei Zhang
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China
- Hebei International Research Center of Medical Engineering and Hebei Provincial Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei, China
| | | | - Dongyang Du
- School of Biomedical Engineering and Guangdong Province Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Yanjun Wu
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China
| | - Zhongxiao Wang
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China
| | - Zhendong Cao
- Department of Radiology, the Affiliated Hospital of Chengde Medical University, Chengde, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Yanli Liu
- Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China
| | | |
Collapse
|
13
|
Modak S, Abdel-Raheem E, Rueda L. Applications of Deep Learning in Disease Diagnosis of Chest Radiographs: A Survey on Materials and Methods. BIOMEDICAL ENGINEERING ADVANCES 2023. [DOI: 10.1016/j.bea.2023.100076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
|
14
|
Rajinikanth V, Vincent PMDR, Srinivasan K, Ananth Prabhu G, Chang CY. A framework to distinguish healthy/cancer renal CT images using the fused deep features. Front Public Health 2023; 11:1109236. [PMID: 36794074 PMCID: PMC9922737 DOI: 10.3389/fpubh.2023.1109236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/04/2023] [Indexed: 02/01/2023] Open
Abstract
Introduction Cancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management. Methods The proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation. Results and discussion This experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.
Collapse
Affiliation(s)
- Venkatesan Rajinikanth
- Division of Research and Innovation, Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India
| | - P. M. Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - G. Ananth Prabhu
- Department of Computer Science Engineering, Sahyadri College of Engineering and Management, Mangaluru, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
- Service Systems Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| |
Collapse
|
15
|
A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
Collapse
|
16
|
Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO. Sci Rep 2022; 12:17403. [PMID: 36257988 PMCID: PMC9579155 DOI: 10.1038/s41598-022-22442-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 10/14/2022] [Indexed: 01/10/2023] Open
Abstract
Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules.
Collapse
|
17
|
Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4608145. [PMID: 36148416 PMCID: PMC9489382 DOI: 10.1155/2022/4608145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/15/2022] [Accepted: 07/27/2022] [Indexed: 11/30/2022]
Abstract
The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients' courses of treatment, has become increasingly widespread in recent years in the field of healthcare. On the other hand, the number of PET scans that are being performed is rising, and radiologists are getting significantly overworked as a result. As a direct result of this, a novel approach that goes by the name “computer-aided diagnostics” is now being investigated as a potential method for reducing the tremendous workloads. A Smart Lung Tumor Detector and Stage Classifier (SLD-SC) is presented in this study as a hybrid technique for PET scans. This detector can identify the stage of a lung tumour. Following the development of the modified LSTM for the detection of lung tumours, the proposed SLD-SC went on to develop a Multilayer Convolutional Neural Network (M-CNN) for the classification of the various stages of lung cancer. This network was then modelled and validated utilising standard benchmark images. The suggested SLD-SC is now being evaluated on lung cancer pictures taken from patients with the disease. We observed that our recommended method gave good results when compared to other tactics that are currently being used in the literature. These findings were outstanding in terms of the performance metrics accuracy, recall, and precision that were assessed. As can be shown by the much better outcomes that were achieved with each of the test images that were used, our proposed method excels its rivals in a variety of respects. In addition to this, it achieves an average accuracy of 97 percent in the categorization of lung tumours, which is much higher than the accuracy achieved by the other approaches.
Collapse
|
18
|
A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7935346. [PMID: 36059415 PMCID: PMC9433214 DOI: 10.1155/2022/7935346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/09/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022]
Abstract
Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K-means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds.
Collapse
|
19
|
Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism. BMC Med Inform Decis Mak 2022; 22:176. [PMID: 35787805 PMCID: PMC9254605 DOI: 10.1186/s12911-022-01919-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 06/23/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. METHODS Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. RESULTS Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. CONCLUSIONS This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.
Collapse
|
20
|
Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9036457. [PMID: 35368941 PMCID: PMC8968389 DOI: 10.1155/2022/9036457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/10/2022] [Accepted: 02/17/2022] [Indexed: 11/20/2022]
Abstract
Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.
Collapse
|
21
|
Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm. Diagnostics (Basel) 2022; 12:diagnostics12020557. [PMID: 35204646 PMCID: PMC8871265 DOI: 10.3390/diagnostics12020557] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/22/2022] [Accepted: 01/30/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women's lives. The best feature optimization is performed to classify the results accurately. The CAD system's accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.
Collapse
|
22
|
A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation. J Pers Med 2022; 12:jpm12020309. [PMID: 35207796 PMCID: PMC8880720 DOI: 10.3390/jpm12020309] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 11/18/2022] Open
Abstract
Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there are still numerous problems that mask extraction techniques need to solve. Thus, the most advanced methods to deploy artificial intelligence (AI) techniques are necessary. The use of cooperative agents in mask extraction increases the efficiency of automatic image segmentation. Hence, we introduce a new mask extraction method that is based on multi-agent deep reinforcement learning (DRL) to minimize the long-term manual mask extraction and to enhance medical image segmentation frameworks. A DRL-based method is introduced to deal with mask extraction issues. This new method utilizes a modified version of the Deep Q-Network to enable the mask detector to select masks from the image studied. Based on COVID-19 computed tomography (CT) images, we used DRL mask extraction-based techniques to extract visual features of COVID-19 infected areas and provide an accurate clinical diagnosis while optimizing the pathogenic diagnostic test and saving time. We collected CT images of different cases (normal chest CT, pneumonia, typical viral cases, and cases of COVID-19). Experimental validation achieved a precision of 97.12% with a Dice of 80.81%, a sensitivity of 79.97%, a specificity of 99.48%, a precision of 85.21%, an F1 score of 83.01%, a structural metric of 84.38%, and a mean absolute error of 0.86%. Additionally, the results of the visual segmentation clearly reflected the ground truth. The results reveal the proof of principle for using DRL to extract CT masks for an effective diagnosis of COVID-19.
Collapse
|
23
|
Two-Stage Hybrid Approach of Deep Learning Networks for Interstitial Lung Disease Classification. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7340902. [PMID: 35155680 PMCID: PMC8826206 DOI: 10.1155/2022/7340902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/14/2022] [Accepted: 01/21/2022] [Indexed: 11/18/2022]
Abstract
High-resolution computed tomography (HRCT) images in interstitial lung disease (ILD) screening can help improve healthcare quality. However, most of the earlier ILD classification work involves time-consuming manual identification of the region of interest (ROI) from the lung HRCT image before applying the deep learning classification algorithm. This paper has developed a two-stage hybrid approach of deep learning networks for ILD classification. A conditional generative adversarial network (c-GAN) has segmented the lung part from the HRCT images at the first stage. The c-GAN with multiscale feature extraction module has been used for accurate lung segmentation from the HRCT images with lung abnormalities. At the second stage, a pretrained ResNet50 has been used to extract the features from the segmented lung image for classification into six ILD classes using the support vector machine classifier. The proposed two-stage algorithm takes a whole HRCT as input eliminating the need for extracting the ROI and classifies the given HRCT image into an ILD class. The performance of the proposed two-stage deep learning network-based ILD classifier has improved considerably due to the stage-wise improvement of deep learning algorithm performance.
Collapse
|
24
|
Ren Z, Zhang Y, Wang S. LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification. Technol Cancer Res Treat 2022; 21:15330338221124372. [PMID: 36148908 PMCID: PMC9511553 DOI: 10.1177/15330338221124372] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Objective: The only possible solution to increase the patients’ fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagnostic techniques. However, deep learning models always get lower performance when the model is overfitting. Methods: We present a Lung Cancer Data Augmented Ensemble (LCDAE) framework to solve the overfitting and lower performance problems in the lung cancer classification tasks. The LCDAE has 3 parts: The Lung Cancer Deep Convolutional GAN, which can synthesize images of lung cancer; A Data Augmented Ensemble model (DA-ENM), which ensembled 6 fine-tuned transfer learning models for training, testing, and validating on a lung cancer dataset; The third part is a Hybrid Data Augmentation (HDA) which combines all the data augmentation techniques in the LCDAE. Results: By comparing with existing state-of-the-art methods, the LCDAE obtains the best accuracy of 99.99%, the precision of 99.99%, and the F1-score of 99.99%. Conclusion: Our proposed LCDAE can overcome the overfitting issue for the lung cancer classification tasks by applying different data augmentation techniques, our method also has the best performance compared to state-of-the-art approaches.
Collapse
Affiliation(s)
- Zeyu Ren
- School of Computing and Mathematical Sciences, 4488University of Leicester, Leicester LE1 7RH, UK
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, 4488University of Leicester, Leicester LE1 7RH, UK
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, 4488University of Leicester, Leicester LE1 7RH, UK
| |
Collapse
|