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Yang D, Miao Y, Liu C, Zhang N, Zhang D, Guo Q, Gao S, Li L, Wang J, Liang S, Li P, Bai X, Zhang K. Advances in artificial intelligence applications in the field of lung cancer. Front Oncol 2024; 14:1449068. [PMID: 39309740 PMCID: PMC11412794 DOI: 10.3389/fonc.2024.1449068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 08/19/2024] [Indexed: 09/25/2024] Open
Abstract
Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.
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Affiliation(s)
- Di Yang
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Yafei Miao
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Changjiang Liu
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Nan Zhang
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Duo Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Qiang Guo
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Shuo Gao
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Information center, Affiliated Hospital of Hebei University, Baoding, China
| | - Linqian Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
| | - Jianing Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Si Liang
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Peng Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Xuan Bai
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Ke Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
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Zhao Z, Guo S, Han L, Wu L, Zhang Y, Yan B. Altruistic seagull optimization algorithm enables selection of radiomic features for predicting benign and malignant pulmonary nodules. Comput Biol Med 2024; 180:108996. [PMID: 39137669 DOI: 10.1016/j.compbiomed.2024.108996] [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: 03/09/2024] [Revised: 05/22/2024] [Accepted: 08/02/2024] [Indexed: 08/15/2024]
Abstract
Accurately differentiating indeterminate pulmonary nodules remains a significant challenge in clinical practice. This challenge becomes increasingly formidable when dealing with the vast radiomic features obtained from low-dose computed tomography, a lung cancer screening technique being rolling out in many areas of the world. Consequently, this study proposed the Altruistic Seagull Optimization Algorithm (AltSOA) for the selection of radiomic features in predicting the malignancy risk of pulmonary nodules. This innovative approach incorporated altruism into the traditional seagull optimization algorithm to seek a global optimal solution. A multi-objective fitness function was designed for training the pulmonary nodule prediction model, aiming to use fewer radiomic features while ensuring prediction performance. Among global radiomic features, the AltSOA identified 11 interested features, including the gray level co-occurrence matrix. This automatically selected panel of radiomic features enabled precise prediction (area under the curve = 0.8383 (95 % confidence interval 0.7862-0.8863)) of the malignancy risk of pulmonary nodules, surpassing the proficiency of radiologists. Furthermore, the interpretability, clinical utility, and generalizability of the pulmonary nodule prediction model were thoroughly discussed. All results consistently underscore the superiority of the AltSOA in predicting the malignancy risk of pulmonary nodules. And the proposed malignant risk prediction model for pulmonary nodules holds promise for enhancing existing lung cancer screening methods. The supporting source codes of this work can be found at: https://github.com/zzl2022/PBMPN.
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Affiliation(s)
- Zhilei Zhao
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Shuli Guo
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Lina Han
- Department of Cardiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Lei Wu
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yating Zhang
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Biyu Yan
- National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
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Liang B, Tong C, Nong J, Zhang Y. Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01152-4. [PMID: 38861072 DOI: 10.1007/s10278-024-01152-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/30/2024] [Accepted: 05/23/2024] [Indexed: 06/12/2024]
Abstract
Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has become pivotal for enhancing diagnostic standards and patient prognosis. In response to the challenges presented by traditional clinical diagnostic methods for NSCLC pathology subtypes, which are invasive, rely on physician experience, and consume medical resources, we explore the potential of radiomics and deep learning to automatically and non-invasively identify NSCLC subtypes from computed tomography (CT) images. An integrated model is proposed that investigates both radiomic features and deep learning features and makes comprehensive decisions based on the combination of these two features. To extract deep features, a three-dimensional convolutional neural network (3D CNN) is proposed to fully utilize the 3D nature of CT images while radiomic features are extracted by radiomics. These two types of features are combined and classified with multi-head attention (MHA) in our proposed model. To our knowledge, this is the first work that integrates different learning methods and features from varied sources in histological subtype classification of lung cancer. Experiments are organized on a mixed dataset comprising NSCLC Radiomics and Radiogenomics. The results show that our proposed model achieves 0.88 in accuracy and 0.89 in the area under the receiver operating characteristic curve (AUC) when distinguishing lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC), indicating the potential of being a non-invasive way for predicting histological subtypes of lung cancer.
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Affiliation(s)
- Baoyu Liang
- School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China
| | - Chao Tong
- School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China.
| | - Jingying Nong
- The Department of Thoracic Surgery, Xuanwu Hospital, Cancer Center of National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, 100053, Beijing, China
| | - Yi Zhang
- The Department of Thoracic Surgery, Xuanwu Hospital, Cancer Center of National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, 100053, Beijing, China
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Sun L, Zhang M, Lu Y, Zhu W, Yi Y, Yan F. Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning. Comput Biol Med 2024; 175:108505. [PMID: 38688129 DOI: 10.1016/j.compbiomed.2024.108505] [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: 12/04/2023] [Revised: 02/28/2024] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
The latest developments in deep learning have demonstrated the importance of CT medical imaging for the classification of pulmonary nodules. However, challenges remain in fully leveraging the relevant medical annotations of pulmonary nodules and distinguishing between the benign and malignant labels of adjacent nodules. Therefore, this paper proposes the Nodule-CLIP model, which deeply mines the potential relationship between CT images, complex attributes of lung nodules, and benign and malignant attributes of lung nodules through a comparative learning method, and optimizes the model in the image feature extraction network by using its similarities and differences to improve its ability to distinguish similar lung nodules. Firstly, we segment the 3D lung nodule information by U-Net to reduce the interference caused by the background of lung nodules and focus on the lung nodule images. Secondly, the image features, class features, and complex attribute features are aligned by contrastive learning and loss function in Nodule-CLIP to achieve lung nodule image optimization and improve classification ability. A series of testing and ablation experiments were conducted on the public dataset LIDC-IDRI, and the final benign and malignant classification rate was 90.6%, and the recall rate was 92.81%. The experimental results show the advantages of this method in terms of lung nodule classification as well as interpretability.
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Affiliation(s)
- Lijing Sun
- College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211800, Jiangsu, China
| | - Mengyi Zhang
- College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211800, Jiangsu, China.
| | - Yu Lu
- College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211800, Jiangsu, China
| | - Wenjun Zhu
- College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211800, Jiangsu, China
| | - Yang Yi
- College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211800, Jiangsu, China
| | - Fei Yan
- Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Nanjing, 210009, Jiangsu, China
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Wang H, Zhu H, Ding L, Yang K. Attention pyramid pooling network for artificial diagnosis on pulmonary nodules. PLoS One 2024; 19:e0302641. [PMID: 38753596 PMCID: PMC11098435 DOI: 10.1371/journal.pone.0302641] [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: 07/07/2023] [Accepted: 04/09/2024] [Indexed: 05/18/2024] Open
Abstract
The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.
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Affiliation(s)
- Hongfeng Wang
- School of Network Engineering, Zhoukou Normal University, Zhoukou, China
| | - Hai Zhu
- School of Network Engineering, Zhoukou Normal University, Zhoukou, China
| | - Lihua Ding
- College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Kaili Yang
- Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Henan University People’s Hospital, Zhengzhou, China
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Zhang X, Yang P, Tian J, Wen F, Chen X, Muhammad T. Classification of benign and malignant pulmonary nodule based on local-global hybrid network. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:689-706. [PMID: 38277335 DOI: 10.3233/xst-230291] [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: 01/28/2024]
Abstract
BACKGROUND The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. OBJECTIVE In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules. METHODS First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features. RESULTS Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%. CONCLUSION The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.
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Affiliation(s)
- Xin Zhang
- Smart City College, Beijing Union University, Beijing, China
| | - Ping Yang
- Smart City College, Beijing Union University, Beijing, China
| | - Ji Tian
- Smart City College, Beijing Union University, Beijing, China
| | - Fan Wen
- Smart City College, Beijing Union University, Beijing, China
| | - Xi Chen
- Smart City College, Beijing Union University, Beijing, China
| | - Tayyab Muhammad
- School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
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Baidya Kayal E, Ganguly S, Sasi A, Sharma S, DS D, Saini M, Rangarajan K, Kandasamy D, Bakhshi S, Mehndiratta A. A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models. Front Oncol 2023; 13:1212526. [PMID: 37671060 PMCID: PMC10476362 DOI: 10.3389/fonc.2023.1212526] [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: 04/26/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000-2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.
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Affiliation(s)
- Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Shuvadeep Ganguly
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Archana Sasi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Swetambri Sharma
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Dheeksha DS
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Manish Saini
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Krithika Rangarajan
- Radiodiagnosis, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | | | - Sameer Bakhshi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, Delhi, India
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Iqbal S, Qureshi AN, Li J, Choudhry IA, Mahmood T. Dynamic learning for imbalanced data in learning chest X-ray and CT images. Heliyon 2023; 9:e16807. [PMID: 37313141 PMCID: PMC10258426 DOI: 10.1016/j.heliyon.2023.e16807] [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: 01/23/2023] [Revised: 05/26/2023] [Accepted: 05/29/2023] [Indexed: 06/15/2023] Open
Abstract
Massive annotated datasets are necessary for networks of deep learning. When a topic is being researched for the first time, as in the situation of the viral epidemic, handling it with limited annotated datasets might be difficult. Additionally, the datasets are quite unbalanced in this situation, with limited findings coming from significant instances of the novel illness. We offer a technique that allows a class balancing algorithm to understand and detect lung disease signs from chest X-ray and CT images. Deep learning techniques are used to train and evaluate images, enabling the extraction of basic visual attributes. The training objects' characteristics, instances, categories, and relative data modeling are all represented probabilistically. It is possible to identify a minority category in the classification process by using an imbalance-based sample analyzer. In order to address the imbalance problem, learning samples from the minority class are examined. The Support Vector Machine (SVM) is used to categorize images in clustering. Physicians and medical professionals can use the CNN model to validate their initial assessments of malignant and benign categorization. The proposed technique for class imbalance (3-Phase Dynamic Learning (3PDL)) and parallel CNN model (Hybrid Feature Fusion (HFF)) for multiple modalities achieve a high F1 score of 96.83 and precision is 96.87, its outstanding accuracy and generalization suggest that it may be utilized to create a pathologist's help tool.
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Affiliation(s)
- Saeed Iqbal
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124,China
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Pakistan
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124,China
- Beijing Engineering Research Center for IoT Software and Systems, 100124, China
| | - Imran Arshad Choudhry
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Pakistan
| | - Tariq Mahmood
- Faculty of Information Sciences, University of Education, Vehari Campus, Vehari, 61100, Pakistan
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586, Kingdom of Saudi Arabia
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