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Wang Z, Wu M, Liu Q, Wang X, Yan C, Song T. Multiclassification of Hepatic Cystic Echinococcosis by Using Multiple Kernel Learning Framework and Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1034-1044. [PMID: 38679514 DOI: 10.1016/j.ultrasmedbio.2024.03.018] [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: 11/22/2023] [Revised: 03/10/2024] [Accepted: 03/30/2024] [Indexed: 05/01/2024]
Abstract
To properly treat and care for hepatic cystic echinococcosis (HCE), it is essential to make an accurate diagnosis before treatment. OBJECTIVE The objective of this study was to assess the diagnostic accuracy of computer-aided diagnosis techniques in classifying HCE ultrasound images into five subtypes. METHODS A total of 1820 HCE ultrasound images collected from 967 patients were included in the study. A multi-kernel learning method was developed to learn the texture and depth features of the ultrasound images. Combined kernel functions were built-in Support Vector Machine (MK-SVM) for the classification work. The experimental results were evaluated using five-fold cross-validation. Finally, our approach was compared with three other machine learning algorithms: the decision tree classifier, random forest, and gradient boosting decision tree. RESULTS Among all the methods used in the study, the MK-SVM achieved the highest accuracy of 96.6% on the fused feature set. CONCLUSION The multi-kernel learning method effectively learns different image features from ultrasound images by utilizing various kernels. The MK-SVM method, which combines the learning of texture features and depth features separately, has significant application value in HCE classification tasks.
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Affiliation(s)
- Zhengye Wang
- Center for Disease Control and Prevention, Xinjiang Production and Construction Corps, Urumqi, China; Ultrasound Department, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Disease in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Miao Wu
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Qian Liu
- Basic Medical College, Xinjiang Medical University, Urumqi, China
| | - Xiaorong Wang
- Ultrasound Department, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Disease in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Chuanbo Yan
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Tao Song
- Ultrasound Department, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Disease in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
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A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
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Naik A, Edla DR. Lung nodule classification using combination of CNN, second and higher order texture features. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.
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Affiliation(s)
- Amrita Naik
- Computer Science and Engineering, National Institute of Technology, Ponda, Goa, India
| | - Damodar Reddy Edla
- Computer Science and Engineering, National Institute of Technology, Ponda, Goa, India
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Monkam P, Qi S, Xu M, Han F, Zhao X, Qian W. CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images. Biomed Eng Online 2018; 17:96. [PMID: 30012167 PMCID: PMC6048884 DOI: 10.1186/s12938-018-0529-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Accepted: 07/10/2018] [Indexed: 12/18/2022] Open
Abstract
Background Early and automatic detection of pulmonary nodules from CT lung screening is the prerequisite for precise management of lung cancer. However, a large number of false positives appear in order to increase the sensitivity, especially for detecting micro-nodules (diameter < 3 mm), which increases the radiologists’ workload and causes unnecessary anxiety for the patients. To decrease the false positive rate, we propose to use CNN models to discriminate between pulmonary micro-nodules and non-nodules from CT image patches. Methods A total of 13,179 micro-nodules and 21,315 non-nodules marked by radiologists are extracted with three different patch sizes (16 × 16, 32 × 32 and 64 × 64) from LIDC/IDRI database and used in the experiments. Three CNN models with different depths (1, 2 or 4 convolutional layers) are designed; their performances are evaluated by the fivefold cross-validation in term of the accuracy, area under the curve (AUC), F-score and sensitivity. The network parameters are also optimized. Results It is found that the performance of the CNN models is greatly dependent on the patches size and the number of convolutional layers. The CNN model with two convolutional layers presented the best performance in case of 32 × 32 patches size, achieving an accuracy of 88.28%, an AUC of 0.87, a F-score of 83.45% and a sensitivity of 83.82%. Conclusions The CNN models with appropriate depth and size of image patches can effectively discriminate between pulmonary micro-nodules and non-nodules, and reduce the false positives and help manage lung cancer precisely.
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Affiliation(s)
- Patrice Monkam
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 195 Chuangxin Avenue, Hunnan District, Shenyang, 110169, China
| | - Shouliang Qi
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 195 Chuangxin Avenue, Hunnan District, Shenyang, 110169, China. .,Key Laboratory of Medical Image Computing of Northeastern University (Ministry of Education), Shenyang, China.
| | - Mingjie Xu
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 195 Chuangxin Avenue, Hunnan District, Shenyang, 110169, China
| | - Fangfang Han
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 195 Chuangxin Avenue, Hunnan District, Shenyang, 110169, China.,Key Laboratory of Medical Image Computing of Northeastern University (Ministry of Education), Shenyang, China
| | - Xinzhuo Zhao
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 195 Chuangxin Avenue, Hunnan District, Shenyang, 110169, China
| | - Wei Qian
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No. 195 Chuangxin Avenue, Hunnan District, Shenyang, 110169, China.,College of Engineering, University of Texas at El Paso, 500W University, El Paso, TX, 79902, USA
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Woźniak M, Połap D, Capizzi G, Sciuto GL, Kośmider L, Frankiewicz K. Small lung nodules detection based on local variance analysis and probabilistic neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 161:173-180. [PMID: 29852959 DOI: 10.1016/j.cmpb.2018.04.025] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Revised: 04/10/2018] [Accepted: 04/26/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination very often help to diagnose some diseases of chest organs. The most frequent cause of wrong diagnosis lie in the radiologist's difficulty in interpreting the presence of lungs carcinoma in chest X-ray. In such circumstances, an automated approach could be highly advantageous as it provides important help in medical diagnosis. METHODS In this paper we propose a new classification method of the lung carcinomas. This method start with the localization and extraction of the lung nodules by computing, for each pixel of the original image, the local variance obtaining an output image (variance image) with the same size of the original image. In the variance image we find the local maxima and then by using the locations of these maxima in the original image we found the contours of the possible nodules in lung tissues. However after this segmentation stage we find many false nodules. Therefore to discriminate the true ones we use a probabilistic neural network as classifier. RESULTS The performance of our approach is 92% of correct classifications, while the sensitivity is 95% and the specificity is 89.7%. The misclassification errors are due to the fact that network confuses false nodules with the true ones (6%) and true nodules with the false ones (2%). CONCLUSIONS Several researchers have proposed automated algorithms to detect and classify pulmonary nodules but these methods fail to detect low-contrast nodules and have a high computational complexity, in contrast our method is relatively simple but at the same time provides good results and can detect low-contrast nodules. Furthermore, in this paper is presented a new algorithm for training the PNN neural networks that allows to obtain PNNs with many fewer neurons compared to the neural networks obtained by using the training algorithms present in the literature. So considerably lowering the computational burden of the trained network and at same time keeping the same performances.
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Affiliation(s)
- Marcin Woźniak
- Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Dawid Połap
- Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Giacomo Capizzi
- Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Grazia Lo Sciuto
- Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Leon Kośmider
- School of Pharmacy with the Division of Laboratory Medicine in Sosnowiec, Department of General and Analytical Chemistry Medical University of Silesia, Jagiellońska 4, Sosnowiec 41-200, Poland.
| | - Katarzyna Frankiewicz
- Specialist Hospital Sz. Starkiewicz in Da̧browa Górnicza, Zagłȩbiowskie Oncology Centre, Szpitalna 13, Da̧browa Górnicza 41-300, Poland.
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