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Usuff R, Kothandapani S, Rangan R, Dhatchnamurthy S. Enhancing radiographic image interpretation: WARES-PRS model for knee bone tumour detection. NETWORK (BRISTOL, ENGLAND) 2024:1-31. [PMID: 38932464 DOI: 10.1080/0954898x.2024.2357660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024]
Abstract
The early diagnosis of tumour is significant in biomedical research field to lower the severity level and restrict the process extension from cancer. Moreover, the detection of early sign of cancer is undertaken with extensive research efforts that dedicated to the disclosure and recognition of tumours. However, the limited data size as well as diverse appearance of images lowered the detection performance and failed to detect complex stage of tumour. So to solve these issues, a Weighted Adaptive Random Ensemble Support Vector-based Partial Reinforcement Search (WARES-PRS) algorithm is proposed that detected bone lesions accurately and also predicted the severity level stage efficiently. Further, the detection is performed with varied stages to diminish the presence of noise and undertaken effective classification. The performance is validated with CNUH dataset that enhanced image pre-processing tasks. Despite the proposed method uncover the mutual relationships between each pixel's local texture and the overall image's global context. The detection and classification efficiency is validated with various measures and the experimental results revealed that the detection accuracy is enhanced for the proposed approach by 98.5%. The outcomes of our study have exhibited a substantial contribution to assisting physicians in the detection of knee bone tumours.
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Affiliation(s)
- Rahamathunnisa Usuff
- School of Information Technology and Engineering Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sudhakar Kothandapani
- Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
| | - Rajesh Rangan
- Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - Saravanan Dhatchnamurthy
- Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
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Salehi MA, Mohammadi S, Harandi H, Zakavi SS, Jahanshahi A, Shahrabi Farahani M, Wu JS. Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:766-777. [PMID: 38343243 PMCID: PMC11031503 DOI: 10.1007/s10278-023-00945-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 04/20/2024]
Abstract
We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted using a combination of keywords related to bone tumors and AI. After extracting contingency tables from all included studies, we performed a meta-analysis using random-effects model to determine the pooled sensitivity and specificity, accompanied by their respective 95% confidence intervals (CI). Quality assessment was evaluated using a modified version of Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST). The pooled sensitivities for AI algorithms and clinicians on internal validation test sets for detecting bone neoplasms were 84% (95% CI: 79.88) and 76% (95% CI: 64.85), and pooled specificities were 86% (95% CI: 81.90) and 64% (95% CI: 55.72), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 84% (95% CI: 75.90) and 91% (95% CI: 83.96), respectively. The same numbers for clinicians were 85% (95% CI: 73.92) and 94% (95% CI: 89.97), respectively. The sensitivity and specificity for clinicians with AI assistance were 95% (95% CI: 86.98) and 57% (95% CI: 48.66). Caution is needed when interpreting findings due to potential limitations. Further research is needed to bridge this gap in scientific understanding and promote effective implementation for medical practice advancement.
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Affiliation(s)
- Mohammad Amin Salehi
- School of Medicine, Tehran University of Medical Sciences, Pour Sina St, Keshavarz Blvd, Tehran, 1417613151, Iran
| | - Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Pour Sina St, Keshavarz Blvd, Tehran, 1417613151, Iran.
| | - Hamid Harandi
- School of Medicine, Tehran University of Medical Sciences, Pour Sina St, Keshavarz Blvd, Tehran, 1417613151, Iran
| | - Seyed Sina Zakavi
- School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Jahanshahi
- School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | | | - Jim S Wu
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA
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Shrivastava A, Nag MK. Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach. Surg Innov 2024; 31:58-70. [PMID: 38059371 DOI: 10.1177/15533506231220968] [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] [Indexed: 12/08/2023]
Abstract
Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest.Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model's .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone.Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.
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Sampath K, Rajagopal S, Chintanpalli A. A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images. Sci Rep 2024; 14:2144. [PMID: 38273131 PMCID: PMC10811327 DOI: 10.1038/s41598-024-52719-8] [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: 02/28/2023] [Accepted: 01/23/2024] [Indexed: 01/27/2024] Open
Abstract
Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages. Prior detection of these lumps or masses can reduce the risk of death and treat bone cancer early. The goal of this current study is to utilize image processing techniques and deep learning-based Convolution neural network (CNN) to classify normal and cancerous bone images. Medical image processing techniques, like pre-processing (e.g., median filter), K-means clustering segmentation, and, canny edge detection were used to detect the cancer region in Computer Tomography (CT) images for parosteal osteosarcoma, enchondroma and osteochondroma types of bone cancer. After segmentation, the normal and cancerous affected images were classified using various existing CNN-based models. The results revealed that AlexNet model showed a better performance with a training accuracy of 98%, validation accuracy of 98%, and testing accuracy of 100%.
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Affiliation(s)
- Kanimozhi Sampath
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Sivakumar Rajagopal
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India.
| | - Ananthakrishna Chintanpalli
- Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India
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Magdy O, Abd Elaziz M, Elgarayhi A, Ewees AA, Sallah M. Bone metastasis detection method based on improving golden jackal optimization using whale optimization algorithm. Sci Rep 2023; 13:15019. [PMID: 37699992 PMCID: PMC10497577 DOI: 10.1038/s41598-023-41733-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: 04/10/2023] [Accepted: 08/30/2023] [Indexed: 09/14/2023] Open
Abstract
This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique's performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes.
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Affiliation(s)
- Omnia Magdy
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt.
- Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt.
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE.
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
- MEU Research Unit, Middle East University, Amman, Jordan.
| | - Ahmed Elgarayhi
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta, 34517, Egypt.
| | - Mohammed Sallah
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
- Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha , 61922, Saudi Arabia
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Li Y, Dong B, Yuan P. The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis. Front Oncol 2023; 13:1207175. [PMID: 37746301 PMCID: PMC10513372 DOI: 10.3389/fonc.2023.1207175] [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: 04/17/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Background Malignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches. Results The inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set. Conclusion Machine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.
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Affiliation(s)
| | - Bo Dong
- Department of Orthopedics, Xi’an Honghui Hospital, Xi’an Jiaotong University, Xi’an Shaanxi, China
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DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction. Diagnostics (Basel) 2023; 13:diagnostics13040757. [PMID: 36832245 PMCID: PMC9955441 DOI: 10.3390/diagnostics13040757] [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: 12/14/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 02/19/2023] Open
Abstract
Among the many different types of cancer, bone cancer is the most lethal and least prevalent. More cases are reported each year. Early diagnosis of bone cancer is crucial since it helps limit the spread of malignant cells and reduce mortality. The manual method of detection of bone cancer is cumbersome and requires specialized knowledge. A deep transfer-based bone cancer diagnosis (DTBV) system using VGG16 feature extraction is proposed to address these issues. The proposed DTBV system uses a transfer learning (TL) approach in which a pre-trained convolutional neural network (CNN) model is used to extract features from the pre-processed input image and a support vector machine (SVM) model is used to train using these features to distinguish between cancerous and healthy bone. The CNN is applied to the image datasets as it provides better image recognition with high accuracy when the layers in neural network feature extraction increase. In the proposed DTBV system, the VGG16 model extracts the features from the input X-ray image. A mutual information statistic that measures the dependency between the different features is then used to select the best features. This is the first time this method has been used for detecting bone cancer. Once selected features are selected, they are fed into the SVM classifier. The SVM model classifies the given testing dataset into malignant and benign categories. A comprehensive performance evaluation has demonstrated that the proposed DTBV system is highly efficient in detecting bone cancer, with an accuracy of 93.9%, which is more accurate than other existing systems.
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Fakieh B, AL-Ghamdi ASALM, Ragab M. Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model. Healthcare (Basel) 2022; 10:healthcare10061040. [PMID: 35742091 PMCID: PMC9222514 DOI: 10.3390/healthcare10061040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 02/04/2023] Open
Abstract
Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert’s reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images.
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Affiliation(s)
- Bahjat Fakieh
- Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (B.F.); (A.S.A.-M.A.-G.)
| | - Abdullah S. AL-Malaise AL-Ghamdi
- Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (B.F.); (A.S.A.-M.A.-G.)
- Information Systems Department, HECI School, Dar Alhekma University, Jeddah 22246, Saudi Arabia
- Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mahmoud Ragab
- Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City, Cairo 11884, Egypt
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Correspondence:
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