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A A, M P, Bourouis S, Band SS, Mosavi A, Agrawal S, Hamdi M. Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images. Front Oncol 2022; 12:834028. [PMID: 35769710 PMCID: PMC9234296 DOI: 10.3389/fonc.2022.834028] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/14/2022] [Indexed: 11/18/2022] Open
Abstract
Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)
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Affiliation(s)
- Ahila A
- Department of Electronics and Communication Engineering, Sethu Institute of Technology, Kariapatti, India
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | - Poongodi M
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | - Sami Bourouis
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- *Correspondence: Ahila A, ; Poongodi M., ; Shahab S. Band, ; Amir Mosavi,
| | | | - Mounir Hamdi
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Shi X, Tang Z, Wang Y, Xie H, Xu L. HOG-SVM Impurity Detection Method for Chinese Liquor (Baijiu) Based on Adaptive GMM Fusion Frame Difference. Foods 2022; 11:1444. [PMID: 35627014 PMCID: PMC9141108 DOI: 10.3390/foods11101444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 11/16/2022] Open
Abstract
Chinese liquor (Baijiu) is one of the four major distilled spirits in the world. At present, liquor products containing impurities still exist on the market, which not only damage corporate image but also endanger consumer health. Due to the production process and packaging technologies, impurities usually appear in products of Baijiu before entering the market, such as glass debris, mosquitoes, aluminium scraps, hair, and fibres. In this paper, a novel method for detecting impurities in bottled Baijiu is proposed. Firstly, the region of interest (ROI) is cropped by analysing the histogram projection of the original image to eliminate redundant information. Secondly, to adjust the number of distributions in the Gaussian mixture model (GMM) dynamically, multiple unmatched distributions are removed and distributions with similar means are merged in the process of modelling the GMM background. Then, to adaptively change the learning rates of the front and background pixels, the learning rate of the pixel model is created by combining the frame difference results of the sequence images. Finally, a histogram of oriented gradient (HOG) features of the moving targets is extracted, and the Support Vector Machine (SVM) model is chosen to exclude bubble interference. The experimental results show that this impurity detection method for bottled Baijiu controls the missed rate by within 1% and the false detection rate by around 3% of impurities. Its speed is five times faster than manual inspection and its repeatability index is good, indicating that the overall performance of the proposed method is better than manual inspection with a lamp. This method is not only efficient and fast, but also provides practical, theoretical, and technical support for impurity detection of bottled Baijiu that has broad application prospects.
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Affiliation(s)
- Xiaoshi Shi
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China; (X.S.); (Z.T.); (Y.W.); (H.X.)
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Zuoliang Tang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China; (X.S.); (Z.T.); (Y.W.); (H.X.)
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Yihan Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China; (X.S.); (Z.T.); (Y.W.); (H.X.)
| | - Hong Xie
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China; (X.S.); (Z.T.); (Y.W.); (H.X.)
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China; (X.S.); (Z.T.); (Y.W.); (H.X.)
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
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Muruganantham P, Balakrishnan SM. Attention Aware Deep Learning Model for Wireless Capsule Endoscopy Lesion Classification and Localization. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00686-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Guo X, Zhang L, Hao Y, Zhang L, Liu Z, Liu J. Multiple abnormality classification in wireless capsule endoscopy images based on EfficientNet using attention mechanism. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2021; 92:094102. [PMID: 34598534 DOI: 10.1063/5.0054161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 08/13/2021] [Indexed: 06/13/2023]
Abstract
The wireless capsule endoscopy (WCE) procedure produces tens of thousands of images of the digestive tract, for which the use of the manual reading process is full of challenges. Convolutional neural networks are used to automatically detect lesions in WCE images. However, studies on clinical multilesion detection are scarce, and it is difficult to effectively balance the sensitivity to multiple lesions. A strategy for detecting multiple lesions is proposed, wherein common vascular and inflammatory lesions can be automatically and quickly detected on capsule endoscopic images. Based on weakly supervised learning, EfficientNet is fine-tuned to extract the endoscopic image features. Combining spatial features and channel features, the proposed attention network is then used as a classifier to obtain three classifications. The accuracy and speed of the model were compared with those of the ResNet121 and InceptionNetV4 models. It was tested on a public WCE image dataset obtained from 4143 subjects. On the computer-assisted diagnosis for capsule endoscopy database, the method gives a sensitivity of 96.67% for vascular lesions and 93.33% for inflammatory lesions. The precision for vascular lesions was 92.80%, and that for inflammatory lesions was 95.73%. The accuracy was 96.11%, which is 1.11% higher than that of the latest InceptionNetV4 network. Prediction for an image only requires 14 ms, which balances the accuracy and speed comparatively better. This strategy can be used as an auxiliary diagnostic method for specialists for the rapid reading of clinical capsule endoscopes.
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Affiliation(s)
- Xudong Guo
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Lulu Zhang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Youguo Hao
- Department of Rehabilitation, Shanghai Putuo People's Hospital, Shanghai 200060, China
| | - Linqi Zhang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zhang Liu
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Jiannan Liu
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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