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Amin J, Anjum MA, Gul N, Sharif M, Kadry S. Clinically acquired new challenging dataset for brain SOL segmentation: AJBDS-2023. Data Brief 2024; 52:109915. [PMID: 38229924 PMCID: PMC10790026 DOI: 10.1016/j.dib.2023.109915] [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: 09/19/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/18/2024] Open
Abstract
Space-occupying lesions (SOL) brain detected on brain MRI are benign and malignant tumors. Several brain tumor segmentation algorithms have been developed but there is a need for a clinically acquired dataset that is used for real-time images. This research is done to facilitate reporting of MRI done for brain tumor detection by incorporating computer-aided detection. Another objective was to make reporting unbiased by decreasing inter-observer errors and expediting daily reporting sessions to decrease radiologists' workload. This is an experimental study. The proposed dataset contains clinically acquired multiplanar, multi-sequential MRI slices (MPMSI) which are used as input to the segmentation model without any preprocessing. The proposed AJBDS-2023 consists of 10667 images of real patients imaging data with a size of 320*320*3. Acquired images have T1W, TW2, Flair, T1W contrast, ADC, and DWI sequences. Pixel-based ground-truth annotated images of the tumor core and edema of 6334 slices are made manually under the supervision of a radiologist. Quantitative assessment of AJBDS-2023 images is done by a novel U-network on 4333 MRI slices. The diagnostic accuracy of our algorithm U-Net trained on AJBDS-2023 was 77.4 precision, 82.3 DSC, 87.4 specificity, 93.8 sensitivity, and 90.4 confidence interval. An experimental analysis of AJBDS-2023 done by the U-Net segmentation model proves that the proposed AJBDS-2023 dataset has images without preprocessing, which is more challenging and provides a more realistic platform for evaluation and analysis of newly developed algorithms in this domain and helps radiologists in MRI brain reporting more realistically.
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Affiliation(s)
- Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | | | - Nadia Gul
- Nadia Gul, FCPS Diagnostic Radiology, Consultant Radiologist POF hospital and Associate Professor of Radiology Wah Medical College, Wah Cantt. Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- MEU Research Unit, Middle East University, Amman 11831, Jordan
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Amin J, Almas Anjum M, Ahmad A, Sharif MI, Kadry S, Kim J. Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization. PeerJ Comput Sci 2024; 10:e1744. [PMID: 38196949 PMCID: PMC10773915 DOI: 10.7717/peerj-cs.1744] [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: 05/01/2023] [Accepted: 11/16/2023] [Indexed: 01/11/2024]
Abstract
Malaria disease can indeed be fatal if not identified and treated promptly. Due to advancements in the malaria diagnostic process, microscopy techniques are employed for blood cell analysis. Unfortunately, the diagnostic process of malaria via microscopy depends on microscopic skills. To overcome such issues, machine/deep learning algorithms can be proposed for more accurate and efficient detection of malaria. Therefore, a method is proposed for classifying malaria parasites that consist of three phases. The bilateral filter is applied to enhance image quality. After that shape-based and deep features are extracted. In shape-based pyramid histograms of oriented gradients (PHOG) features are derived with the dimension of N × 300. Deep features are derived from the residual network (ResNet)-50, and ResNet-18 at fully connected layers having the dimension of N × 1,000 respectively. The features obtained are fused serially, resulting in a dimensionality of N × 2,300. From this set, N × 498 features are chosen using the generalized normal distribution optimization (GNDO) method. The proposed method is accessed on a microscopic malarial parasite imaging dataset providing 99% classification accuracy which is better than as compared to recently published work.
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Affiliation(s)
- Javeria Amin
- University of Wah, Department of Computer Science, Wah Cantt, Pakistan
| | | | - Abraz Ahmad
- University of Wah, Department of Computer Science, Wah Cantt, Pakistan
| | - Muhammad Irfan Sharif
- Department of Information Sciences, University of Education Lahore, Jauharabad Campus, Jauharabad, Pakistan
| | - Seifedine Kadry
- Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE
- MEU Research Unit, Middle East University, Amman, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Jungeun Kim
- Department of Software, Kongju National University, Cheonan, Korea
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Zhang Q, Cheng J, Zhou C, Jiang X, Zhang Y, Zeng J, Liu L. PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation. Front Physiol 2023; 14:1259877. [PMID: 37711463 PMCID: PMC10498772 DOI: 10.3389/fphys.2023.1259877] [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: 07/17/2023] [Accepted: 08/16/2023] [Indexed: 09/16/2023] Open
Abstract
Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People's Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).
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Affiliation(s)
- Qile Zhang
- Department of Rehabilitation, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
| | - Jianzhen Cheng
- Department of Rehabilitation, Quzhou Third Hospital, Quzhou, China
| | - Chun Zhou
- Department of Rehabilitation, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
| | - Xiaoliang Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou, China
| | - Yuanxiang Zhang
- College of Mechanical Engineering, Quzhou University, Quzhou, China
| | - Jiantao Zeng
- College of Mechanical Engineering, Quzhou University, Quzhou, China
| | - Li Liu
- Department of Thyroid and Breast Surgery, Kecheng District People’s Hospital, Quzhou, China
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Vinodkumar PK, Karabulut D, Avots E, Ozcinar C, Anbarjafari G. A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040635. [PMID: 37190423 PMCID: PMC10137403 DOI: 10.3390/e25040635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023]
Abstract
The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.
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Affiliation(s)
| | - Dogus Karabulut
- iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia
| | - Egils Avots
- iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia
| | - Cagri Ozcinar
- iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia
| | - Gholamreza Anbarjafari
- iCV Lab, Institute of Technology, University of Tartu, 50090 Tartu, Estonia
- PwC Advisory, 00180 Helsinki, Finland
- iVCV OÜ, 51011 Tartu, Estonia
- Institute of Higher Education, Yildiz Technical University, Beşiktaş, Istanbul 34349, Turkey
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Wei L, Liu H, Xu J, Shi L, Shan Z, Zhao B, Gao Y. Quantum machine learning in medical image analysis: A Survey. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7453935. [PMID: 36590845 PMCID: PMC9803565 DOI: 10.1155/2022/7453935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/26/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Abstract
In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.
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Amin J, Sharif M, Mallah GA, Fernandes SL. An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification. Front Public Health 2022; 10:969268. [PMID: 36148344 PMCID: PMC9486170 DOI: 10.3389/fpubh.2022.969268] [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: 06/14/2022] [Accepted: 08/03/2022] [Indexed: 01/25/2023] Open
Abstract
Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.
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Affiliation(s)
- Javeria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan,*Correspondence: Javeria Amin
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ghulam Ali Mallah
- Department of Computer Science, Shah Abdul Latif University, Khairpur, Pakistan
| | - Steven L. Fernandes
- Department of Computer Science, Design and Journalism, Creighton University, Omaha, NE, United States
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Yunus U, Amin J, Sharif M, Yasmin M, Kadry S, Krishnamoorthy S. Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network. LIFE (BASEL, SWITZERLAND) 2022; 12:life12081126. [PMID: 36013305 PMCID: PMC9410095 DOI: 10.3390/life12081126] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 12/23/2022]
Abstract
Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.
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Affiliation(s)
- Usman Yunus
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan; (U.Y.); (M.S.); (M.Y.)
| | - Javeria Amin
- Department of Computer Science, University of Wah, Wah Cantt 47010, Pakistan;
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan; (U.Y.); (M.S.); (M.Y.)
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan; (U.Y.); (M.S.); (M.Y.)
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
| | - Sujatha Krishnamoorthy
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325060, China
- Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325060, China
- Correspondence:
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