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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.
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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
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2
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Shekhar M, Khetavath S. An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images. J Med Eng Technol 2024; 48:121-150. [PMID: 39282826 DOI: 10.1080/03091902.2024.2399015] [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: 04/25/2024] [Revised: 08/16/2024] [Accepted: 08/24/2024] [Indexed: 10/10/2024]
Abstract
An early detection of lung tumors is critical for better treatment results, and CT scans can reveal lumps in the lungs which are too small to be picked up by conventional X-rays. CT imaging has advantages, but it also exposes a person to radiation from ions, which raises the possibility of malignancy, particularly when the imaging procedure is done. Access to expensive-quality CT scans and the related sophisticated analytic tools might be restricted in environments with fewer resources due to their high cost and limited availability. It will need an array of creative technological innovations to overcome such weaknesses. This paper aims to design a heuristic and deep learning-aided lung cancer classification using CT images. The collected images are undergone for segmentation, which is performed by Shuffling Atrous Convolutional (SAC) based ResUnet++ (SACRUnet++). Finally, the lung cancer classification is performed by the Adaptive Residual Attention Network (ARAN) by inputting the segmented images. Here the parameters of ARAN are optimally tuned using the Improved Garter Snake Optimization Algorithm (IGSOA). The developed lung cancer classification performance is compared to conventional lung cancer classification models and it showed high accuracy.
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Affiliation(s)
- Maloth Shekhar
- Electronics and Communication Engineering, Chaitanya Deemed to be University, Warangal, India
| | - Seetharam Khetavath
- Electronics and Communication Engineering, Chaitanya Deemed to be University, Warangal, India
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3
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Zhang J, Zou W, Hu N, Zhang B, Wang J. S-Net: an S-shaped network for nodule detection in 3D CT images. Phys Med Biol 2024; 69:075013. [PMID: 38382097 DOI: 10.1088/1361-6560/ad2b96] [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: 08/20/2023] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Objective. Accurate and automatic detection of pulmonary nodules is critical for early lung cancer diagnosis, and promising progress has been achieved in developing effective deep models for nodule detection. However, most existing nodule detection methods merely focus on integrating elaborately designed feature extraction modules into the backbone of the detection network to extract rich nodule features while ignore disadvantages of the structure of detection network itself. This study aims to address these disadvantages and develop a deep learning-based algorithm for pulmonary nodule detection to improve the accuracy of early lung cancer diagnosis.Approach. In this paper, an S-shaped network called S-Net is developed with the U-shaped network as backbone, where an information fusion branch is used to propagate lower-level details and positional information critical for nodule detection to higher-level feature maps, head shared scale adaptive detection strategy is utilized to capture information from different scales for better detecting nodules with different shapes and sizes and the feature decoupling detection head is used to allow the classification and regression branches to focus on the information required for their respective tasks. A hybrid loss function is utilized to fully exploit the interplay between the classification and regression branches.Main results. The proposed S-Net network with ResSENet and other three U-shaped backbones from SANet, OSAF-YOLOv3 and MSANet (R+SC+ECA) models achieve average CPM scores of 0.914, 0.915, 0.917 and 0.923 on the LUNA16 dataset, which are significantly higher than those achieved with other existing state-of-the-art models.Significance. The experimental results demonstrate that our proposed method effectively improves nodule detection performance, which implies potential applications of the proposed method in clinical practice.
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Affiliation(s)
- JingYu Zhang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China
| | - Wei Zou
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China
| | - Nan Hu
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China
| | - Bin Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou 215006, People's Republic of China
| | - Jiajun Wang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, People's Republic of China
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4
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Siddiqui EA, Chaurasia V, Shandilya M. Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter. J Cancer Res Clin Oncol 2023; 149:11279-11294. [PMID: 37368121 DOI: 10.1007/s00432-023-04992-9] [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: 05/03/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023]
Abstract
Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique's performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate.
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Affiliation(s)
| | | | - Madhu Shandilya
- Maulana Azad National Institute of Technology, Bhopal, 462003, India
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5
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M DL, M DP. An Improved Convolution Neural Network and Modified Regularized K-Means-Based Automatic Lung Nodule Detection and Classification. J Digit Imaging 2023; 36:1431-1446. [PMID: 37106212 PMCID: PMC10406790 DOI: 10.1007/s10278-023-00809-w] [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: 10/30/2022] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 04/29/2023] Open
Abstract
If lung cancer is not detected in its initial phases, it can be fatal. However, because of the quantity and structure of its nodules, lung cancer is difficult to detect early. For accurate detections, radiologists require assistance from automated tools. Numerous expert methods have been created over time to assist radiologists in the diagnosis of lung cancer. However, this requires accurate research. Therefore, in this article, we propose a framework to precisely detect lung cancer by categorizing it between benign and malignant nodules. To achieve this objective, an efficient deep-learning algorithm is presented. The presented technique consists of four stages, namely pre-processing, segmentation, classification, and severity stage analysis. Initially, the collected image is given to the pre-processing stage to eliminate the distortion present in the image. Then, the noise-free image is given to the segmentation stage. For segmentation, in this paper, modified regularized K-means (MRKM) clustering algorithm is presented. After the segmentation process, the segmented nodule image is fed to the classification stage to categorize the nodule as benign or malignant (risk nodule). For classification, an improved convolution neural network (ICNN) is presented. The proposed ICNN is designed by modifying CNN with the integration of the adaptive tree seed optimization (ATSO) algorithm. Finally, the stage identification is carried out based on the size of the nodule and we classify the malignant nodule as S1-S4. The presented technique attained the maximum accuracy of 96.5% and performance compared with existing state-of-art methods.
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Affiliation(s)
- Dhasny Lydia M
- Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu India
| | - Dr. Prakash M
- Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu India
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6
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Javed MA, Bin Liaqat H, Meraj T, Alotaibi A, Alshammari M. Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:6357252. [PMID: 37538561 PMCID: PMC10396675 DOI: 10.1155/2023/6357252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/07/2022] [Accepted: 09/26/2022] [Indexed: 08/05/2023]
Abstract
Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient's survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.
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Affiliation(s)
| | - Hannan Bin Liaqat
- Department of Information Technology, Division of Science and Technology University of Education, Township Campus Lahore, Lahore, Pakistan
| | - Talha Meraj
- Department of Computer Science, COMSATS University Islamabad—Wah Campus, Wah Cantt, Rawalpindi 47040, Pakistan
| | - Aziz Alotaibi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Majid Alshammari
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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7
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Cao Z, Li R, Yang X, Fang L, Li Z, Li J. Multi-scale detection of pulmonary nodules by integrating attention mechanism. Sci Rep 2023; 13:5517. [PMID: 37015969 PMCID: PMC10073202 DOI: 10.1038/s41598-023-32312-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/25/2023] [Indexed: 04/06/2023] Open
Abstract
The detection of pulmonary nodules has a low accuracy due to the various shapes and sizes of pulmonary nodules. In this paper, a multi-scale detection network for pulmonary nodules based on the attention mechanism is proposed to accurately predict pulmonary nodules. During data processing, the pseudo-color processing strategy is designed to enhance the gray image and introduce more contextual semantic information. In the feature extraction network section, this paper designs a basic module of ResSCBlock integrating attention mechanism for feature extraction. At the same time, the feature pyramid structure is used for feature fusion in the network, and the problem of the detection of small-size nodules which are easily lost is solved by multi-scale prediction method. The proposed method is tested on the LUNA16 data set, with an 83% mAP value. Compared with other detection networks, the proposed method achieves an improvement in detecting pulmonary nodules.
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Affiliation(s)
- Zhenguan Cao
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Rui Li
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
| | - Xun Yang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Liao Fang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Zhuoqin Li
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Jinbiao Li
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
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8
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Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A, Khan MWJ, Szczuko P. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends. INFORMATION FUSION 2023. [DOI: 10.1016/j.inffus.2022.09.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
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9
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Göreke V. A novel method based on Wiener filter for denoising Poisson noise from medical X-Ray images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Huang SY, Hsu WL, Hsu RJ, Liu DW. Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey. Diagnostics (Basel) 2022; 12:diagnostics12112765. [PMID: 36428824 PMCID: PMC9689961 DOI: 10.3390/diagnostics12112765] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/19/2022] [Accepted: 11/04/2022] [Indexed: 11/16/2022] Open
Abstract
There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for future development, including model development, data augmentation processing, and dataset creation. The strengths and deficiencies of studies on models and data augmentation, as well as their application to medical image segmentation, were analyzed. Fully convolutional network developments have led to the creation of the U-Net and its derivatives. Another noteworthy image segmentation model is DeepLab. Regarding data augmentation, due to the low data volume of medical images, most studies focus on means to increase the wealth of medical image data. Generative adversarial networks (GAN) increase data volume via deep learning. Despite the increasing types of medical image datasets, there is still a deficiency of datasets on specific problems, which should be improved moving forward. Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied.
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Affiliation(s)
- Sheng-Yao Huang
- Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan
- Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan
| | - Wen-Lin Hsu
- Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan
- Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan
- School of Medicine, Tzu Chi University, Hualien 97071, Taiwan
| | - Ren-Jun Hsu
- Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan
- Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan
- School of Medicine, Tzu Chi University, Hualien 97071, Taiwan
- Correspondence: (R.-J.H.); (D.-W.L.); Tel. & Fax: +886-3-8561825 (R.-J.H. & D.-W.L.)
| | - Dai-Wei Liu
- Institute of Medical Science, Tzu Chi University, Hualien 97071, Taiwan
- Department of Radiation Oncology, Hualien Tzu Chi General Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan
- Cancer Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97071, Taiwan
- School of Medicine, Tzu Chi University, Hualien 97071, Taiwan
- Correspondence: (R.-J.H.); (D.-W.L.); Tel. & Fax: +886-3-8561825 (R.-J.H. & D.-W.L.)
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11
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Clustering based lung lobe segmentation and optimization based lung cancer classification using CT images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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12
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Rahman H, Bukht TFN, Imran A, Tariq J, Tu S, Alzahrani A. A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet. Bioengineering (Basel) 2022; 9:bioengineering9080368. [PMID: 36004893 PMCID: PMC9404984 DOI: 10.3390/bioengineering9080368] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 11/25/2022] Open
Abstract
According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.
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Affiliation(s)
- Hameedur Rahman
- Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China
| | | | - Azhar Imran
- Department of Creative Technologies, Faculty of Computing & AI, Air University PAF Complex, Islamabad 44000, Pakistan
| | - Junaid Tariq
- Department of Computer Science, National University of Modern Languages (NUML), Rawalpindi Campus, Islamabad 44000, Pakistan
| | - Shanshan Tu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China
- Correspondence:
| | - Abdulkareeem Alzahrani
- Computer Engineering and Science Department, Faculty of Computer Science and Information Technology, Al Baha University, Al Baha 65515, Saudi Arabia
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Albahli S, Meraj T, Chakraborty C, Rauf HT. AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:37569-37589. [PMID: 35968412 PMCID: PMC9362623 DOI: 10.1007/s11042-022-13499-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/29/2021] [Accepted: 07/13/2022] [Indexed: 05/27/2023]
Abstract
To identify various pneumonia types, a gap of 15% value is being created every five years. To fill this gap, accurate detection of chest disease is required in the healthcare department to avoid any serious issues in the future. Testing the affected lungs to detect a Coronavirus 2019 (COVID-19) using the same imaging modalities may detect some other chest diseases. This wrong diagnosis strongly needs a multidisciplinary approach to the right diagnosis of chest-related diseases. Only a few works till now are targeting pathological x-ray images. Many studies target only a single chest disease that is not enough to automate chest disease detection. Only a few studies regarding the observation of the COVID-19, but more cases are those where it can be misclassified as detecting techniques not providing any generic solution for all types of chest diseases. However, the existing studies can only detect if the person has COVID-19 or not. The proposed work significantly contributes to detecting COVID-19 and other chest diseases by providing useful analysis of chest-related diseases. One of our testing approaches achieves 90.22% accuracy for 15 types of chest disease with 100% correct classification of COVID-19. Though it analyzes the perfect detection as the accuracy level is high enough, but it would be an excellent decision to consider the proposed study until doctors can visually inspect the input images used by models that lead to its detection.
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Affiliation(s)
- Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
| | - Talha Meraj
- Department of Computer Science, COMSATS University Islamabad - Wah Campus, 47040 Wah Cantt, Pakistan
| | | | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, UK
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14
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Javeed A, Ali L, Mohammed Seid A, Ali A, Khan D, Imrana Y. A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1901735. [PMID: 35707186 PMCID: PMC9192258 DOI: 10.1155/2022/1901735] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 04/16/2022] [Indexed: 12/14/2022]
Abstract
Nowadays, caesarean section (CS) is given preference over vaginal birth and this trend is rapidly rising around the globe, although CS has serious complications such as pregnancy scar, scar dehiscence, and morbidly adherent placenta. Thus, CS should only be performed when it is absolutely necessary for mother and fetus. To avoid unnecessary CS, researchers have developed different machine-learning- (ML-) based clinical decision support systems (CDSS) for CS prediction using electronic health record of the pregnant women. However, previously proposed methods suffer from the problems of poor accuracy and biasedness in ML. To overcome these problems, we have designed a novel CDSS where random oversampling example (ROSE) technique has been used to eliminate the problem of minority classes in the dataset. Furthermore, principal component analysis has been employed for feature extraction from the dataset while, for classification purpose, random forest (RF) model is deployed. We have fine-tuned the hyperparameter of RF using a grid search algorithm for optimal classification performance. Thus, the newly proposed system is named ROSE-PCA-RF and it is trained and tested using an online CS dataset available on the UCI repository. In the first experiment, conventional RF model is trained and tested on the dataset while in the second experiment, the proposed model is tested. The proposed ROSE-PCA-RF model improved the performance of traditional RF by 4.5% with reduced time complexity, while only using two extracted features through the PCA. Moreover, the proposed model has obtained 96.29% accuracy on training data while improving the accuracy of 97.12% on testing data.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institute, Solna, Sweden
| | - Liaqat Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Bannu, Pakistan
| | - Abegaz Mohammed Seid
- Information & Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Bannu, Pakistan
| | - Dilpazir Khan
- Department of Computer Science, University of Science and Technology Bannu, Bannu, Pakistan
| | - Yakubu Imrana
- School of Engineering, University for Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
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15
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Shafi I, Aziz A, Din S, Ashraf I. Reduced features set neural network approach based on high-resolution time-frequency images for cardiac abnormality detection. Comput Biol Med 2022; 145:105425. [DOI: 10.1016/j.compbiomed.2022.105425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/03/2022]
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16
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Lu S, Huang K, Meraj T, Rauf HT. A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks. PeerJ Comput Sci 2022; 8:e879. [PMID: 35494833 PMCID: PMC9044336 DOI: 10.7717/peerj-cs.879] [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: 08/06/2021] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA's need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Network (CNN) design still need more confident validation and multi-aspect covering feature schemes. Several public datasets are available of text-based CAPTCHa, including Kaggle and other dataset repositories where self-generation of CAPTCHA datasets are available. The previous studies are dataset-specific only and cannot perform well on other CAPTCHA's. Therefore, the proposed study uses two publicly available datasets of 4- and 5-character text-based CAPTCHA images to propose a CAPTCHA solver. Furthermore, the proposed study used a skip-connection-based CNN model to solve a CAPTCHA. The proposed research employed 5-folds on data that delivers 10 different CNN models on two datasets with promising results compared to the other studies.
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Affiliation(s)
- Shida Lu
- State Grid Information & Communication Company, SMEPC, Shanghai, China
| | - Kai Huang
- Shanghai Shineenergy Information Technology Development Co., Ltd., Shanghai, China
| | - Talha Meraj
- COMSATS Institute of Information Technology, Islamabad, Pakistan
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17
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A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach. ELECTRONICS 2022. [DOI: 10.3390/electronics11050756] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.
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18
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Javeed A, Khan SU, Ali L, Ali S, Imrana Y, Rahman A. Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9288452. [PMID: 35154361 PMCID: PMC8831075 DOI: 10.1155/2022/9288452] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/15/2022] [Indexed: 12/13/2022]
Abstract
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Sweden
| | - Shafqat Ullah Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan
| | - Liaqat Ali
- Department of Electronics, University of Buner, Buner, Pakistan
| | - Sardar Ali
- School of Engineering and Applied Sciences, Isra University Islamabad Campus, Pakistan
| | - Yakubu Imrana
- School of Engineering, University of Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Atiqur Rahman
- Department of Computer Science, University of Science and Technology Bannu, Pakistan
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19
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Meraj T, Alosaimi W, Alouffi B, Rauf HT, Kumar SA, Damaševičius R, Alyami H. A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data. PeerJ Comput Sci 2021; 7:e805. [PMID: 35036531 PMCID: PMC8725669 DOI: 10.7717/peerj-cs.805] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/12/2021] [Indexed: 06/14/2023]
Abstract
Breast cancer is one of the leading causes of death in women worldwide-the rapid increase in breast cancer has brought about more accessible diagnosis resources. The ultrasonic breast cancer modality for diagnosis is relatively cost-effective and valuable. Lesion isolation in ultrasonic images is a challenging task due to its robustness and intensity similarity. Accurate detection of breast lesions using ultrasonic breast cancer images can reduce death rates. In this research, a quantization-assisted U-Net approach for segmentation of breast lesions is proposed. It contains two step for segmentation: (1) U-Net and (2) quantization. The quantization assists to U-Net-based segmentation in order to isolate exact lesion areas from sonography images. The Independent Component Analysis (ICA) method then uses the isolated lesions to extract features and are then fused with deep automatic features. Public ultrasonic-modality-based datasets such as the Breast Ultrasound Images Dataset (BUSI) and the Open Access Database of Raw Ultrasonic Signals (OASBUD) are used for evaluation comparison. The OASBUD data extracted the same features. However, classification was done after feature regularization using the lasso method. The obtained results allow us to propose a computer-aided design (CAD) system for breast cancer identification using ultrasonic modalities.
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Affiliation(s)
- Talha Meraj
- Department of Computer Science, COMSATS University Islamabad-Wah Campus, Wah Cantt, Pakistan
| | - Wael Alosaimi
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Bader Alouffi
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Hafiz Tayyab Rauf
- Department of Computer Science, Faculty of Engineering & Informatics, University of Bradford, Bradford, United Kingdom
| | - Swarn Avinash Kumar
- Department of Information Technology, Indian Institute of Information Technology, Uttar Pradesh, Jhalwa, Prayagraj, India
| | | | - Hashem Alyami
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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20
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Arshad M, Khan MA, Tariq U, Armghan A, Alenezi F, Younus Javed M, Aslam SM, Kadry S. A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9619079. [PMID: 34912449 PMCID: PMC8668359 DOI: 10.1155/2021/9619079] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 10/28/2021] [Accepted: 11/10/2021] [Indexed: 11/28/2022]
Abstract
In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of the most dangerous types of skin cancer, and its survival rate is 5%. The development of skin cancer has risen over the last couple of years. Early identification of skin cancer can help reduce the human mortality rate. Dermoscopy is a technology used for the acquisition of skin images. However, the manual inspection process consumes more time and required much cost. The recent development in the area of deep learning showed significant performance for classification tasks. In this research work, a new automated framework is proposed for multiclass skin lesion classification. The proposed framework consists of a series of steps. In the first step, augmentation is performed. For the augmentation process, three operations are performed: rotate 90, right-left flip, and up and down flip. In the second step, deep models are fine-tuned. Two models are opted, such as ResNet-50 and ResNet-101, and updated their layers. In the third step, transfer learning is applied to train both fine-tuned deep models on augmented datasets. In the succeeding stage, features are extracted and performed fusion using a modified serial-based approach. Finally, the fused vector is further enhanced by selecting the best features using the skewness-controlled SVR approach. The final selected features are classified using several machine learning algorithms and selected based on the accuracy value. In the experimental process, the augmented HAM10000 dataset is used and achieved an accuracy of 91.7%. Moreover, the performance of the augmented dataset is better as compared to the original imbalanced dataset. In addition, the proposed method is compared with some recent studies and shows improved performance.
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Affiliation(s)
- Mehak Arshad
- Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
| | | | - Usman Tariq
- College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj, Saudi Arabia
| | - Ammar Armghan
- Department of Electrical Engineering, Jouf University, Sakaka 75471, Saudi Arabia
| | - Fayadh Alenezi
- Department of Electrical Engineering, Jouf University, Sakaka 75471, Saudi Arabia
| | | | - Shabnam Mohamed Aslam
- Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia
| | - Seifedine Kadry
- Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway
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21
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Khan MA, Rajinikanth V, Satapathy SC, Taniar D, Mohanty JR, Tariq U, Damaševičius R. VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images. Diagnostics (Basel) 2021; 11:2208. [PMID: 34943443 PMCID: PMC8699868 DOI: 10.3390/diagnostics11122208] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/27/2022] Open
Abstract
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.
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Affiliation(s)
| | - Venkatesan Rajinikanth
- Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamilnadu 600119, India;
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - David Taniar
- Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| | - Jnyana Ranjan Mohanty
- School of Computer Applications, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - Usman Tariq
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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22
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Baressi Šegota S, Lorencin I, Smolić K, Anđelić N, Markić D, Mrzljak V, Štifanić D, Musulin J, Španjol J, Car Z. Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach. BIOLOGY 2021; 10:biology10111134. [PMID: 34827126 PMCID: PMC8614660 DOI: 10.3390/biology10111134] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 01/11/2023]
Abstract
Simple Summary Bladder cancer is a common cancer of the urinary tract, characterized by high metastatic potential and recurrence. The research applies a transfer learning approach on CT images (frontal, axial, and saggital axes) for the purpose of semantic segmentation of areas affected by bladder cancer. A system consisting of AlexNet network for plane recognition, using transfer learning-based U-net networks for the segmentation task. Achieved results show that the proposed system has a high performance, suggesting possible use in clinical practice. Abstract Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro¯ of 0.9999 and σ(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC¯ up to 0.9587 and σ(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC¯ up to 0.9372 and σ(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC¯ values up to 0.9660 with a σ(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.
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Affiliation(s)
- Sandi Baressi Šegota
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Ivan Lorencin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Klara Smolić
- Clinical Hospital Center Rijeka, Krešimirova 42, 51000 Rijeka, Croatia; (K.S.); (D.M.); (J.Š.)
| | - Nikola Anđelić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Dean Markić
- Clinical Hospital Center Rijeka, Krešimirova 42, 51000 Rijeka, Croatia; (K.S.); (D.M.); (J.Š.)
- Faculty of Medicine, Branchetta 20/1, University of Rijeka, 51000 Rijeka, Croatia
| | - Vedran Mrzljak
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
- Correspondence: ; Tel.: +385-51-651551
| | - Daniel Štifanić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Jelena Musulin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
| | - Josip Španjol
- Clinical Hospital Center Rijeka, Krešimirova 42, 51000 Rijeka, Croatia; (K.S.); (D.M.); (J.Š.)
- Faculty of Medicine, Branchetta 20/1, University of Rijeka, 51000 Rijeka, Croatia
| | - Zlatan Car
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (S.B.Š.); (I.L.); (N.A.); (D.Š.); (J.M.); (Z.C.)
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23
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Irfan R, Almazroi AA, Rauf HT, Damaševičius R, Nasr EA, Abdelgawad AE. Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion. Diagnostics (Basel) 2021; 11:1212. [PMID: 34359295 PMCID: PMC8304124 DOI: 10.3390/diagnostics11071212] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/16/2021] [Accepted: 04/27/2021] [Indexed: 12/15/2022] Open
Abstract
Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.
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Affiliation(s)
- Rizwana Irfan
- Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 21959, Saudi Arabia; (R.I.); (A.A.A.)
| | - Abdulwahab Ali Almazroi
- Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 21959, Saudi Arabia; (R.I.); (A.A.A.)
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland;
| | - Emad Abouel Nasr
- Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia; (E.A.N.); (A.E.A.)
| | - Abdelatty E. Abdelgawad
- Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia; (E.A.N.); (A.E.A.)
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24
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Sadad T, Rehman A, Hussain A, Abbasi AA, Khan MQ. A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques. Curr Med Imaging 2020; 17:686-694. [PMID: 33334293 DOI: 10.2174/1573405616666201217112521] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/07/2020] [Accepted: 07/23/2020] [Indexed: 12/24/2022]
Abstract
Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modalities, such as complex background, low contrast, brightness issues, poorly defined borders and the shape of the affected area. Recently, computer-aided diagnosis (CAD) models have been used to accurately diagnose tumors in different parts of the human body, especially breast, brain, lung, liver, skin and colon cancers. These cancers are diagnosed using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), colonoscopy, mammography, dermoscopy and histopathology. The aim of this review was to investigate existing approaches for the diagnosis of breast, brain, lung, liver, skin and colon tumors. The review focuses on decision-making systems, including handcrafted features and deep learning architectures for tumor detection.
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Affiliation(s)
- Tariq Sadad
- Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan
| | - Amjad Rehman
- Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan
| | - Aaqif Afzaal Abbasi
- Department of Software Engineering, Foundation University, Islamabad, Pakistan
| | - Muhammad Qasim Khan
- Department of Computer Science, COMSATS University (Attock Campus) Islamabad, Pakistan
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