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Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

IET IMAGE PROCESSING 2020年第14期14卷 3355-3365页

作者： Vijila Rani, K. Joseph Jawhar, S. Arunachala Coll Engn Women ECE Nagercoil 629203 TN India Arunachala Coll Engn Women EEE Nagercoil 629203 TN India

A chronic disorder caused by abnormal growth of the lung cells in the pulmonary tumour. This study suggests a modern automated approach to improve efficiency and decrease the difficulty of lung tumour diagnosis. The proposed algorithm for lung tumour sensing consists of four phases: pre-processing, segmentation, extraction, and characteristics classification. The first stage is the image acquisition here input lung image is read and then resized. The second stage is the image pre-processing here Perona-Malik diffusion with unsharp masking filter is proposed for enhancement purposes. The third stage is the segmentation here the improved histogram-based fast 2D Otsu's thresholding is proposed for lung tumour segmentation purposes. Finally, linear discriminant analysis classifier, support vector machine (SVM) classifier, SVM-sequence minimal optimisation classifier, Naive Bayes classifier, SVM-advance sequence minimal optimisation (SVM-ASMO) classification [proposed] included in the various classifier groups adopted in this report. Overall performance accuracy of 0.962 is obtained using the proposed SVM-ASMO method that helps to diagnose the cancer cells using the feature extraction process, which is done automatically. The specificity, precision, recall, and F1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984,respectively.

关键词： pattern classification medical image processing image segmentation cancer image classification Bayes methods feature extraction computerised tomography optimisation support vector machines image enhancement tumours lung automatic segmentation advance sequential minimal optimisation techniques lung cells pulmonary tumour successful treatment planning early tumour detection lung tumour diagnosis lung tumour sensing characteristics classification image acquisition here input lung image improved histogram-based fast 2D Otsu's thresholding lung tumour segmentation purposes linear discriminant analysis classifier support vector machine classifier SVM-sequence minimal optimisation classifier SVM-advance sequence minimal optimisation classification

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Automated detection of myocardial infarction from ECG signal using variational mode decomposition based analysis

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HEALTHCARE TECHNOLOGY LETTERS 2020年第6期7卷 155-160页

作者： Kapfo, Ato Dandapat, Samarendra Bora, Prabin Kumar Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 Assam India

In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode, and the PCs provide the representation of the ECG signal with less redundancy. K-nearest neighbour and support vector machine classifier are utilised to assess the performance of the extracted features for the detection and classification of MI and normal (healthy control). The proposed method achieved a specificity of 99.88%, sensitivity of 99.90%, and accuracy of 99.88%. Experimental results demonstrate that the proposed method with the multiscale mode energy and PC features achieved better output compared to the previously published work.

关键词： feature extraction signal classification principal component analysis medical signal processing covariance matrices medical signal detection support vector machines electrocardiography support vector machine classifier classification MI multiscale mode energy PC automated detection myocardial infarction ECG signal variational mode decomposition method diagnostic information electrocardiogram signal principal component multiscale covariance matrices mode energies neighbour

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Novel breast cancer classification framework based on deep learning

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IET IMAGE PROCESSING 2020年第13期14卷 3254-3259页

作者： Salama, Wessam M. Elbagoury, Azza M. Aly, Moustafa H. Pharos Univ Fac Engn Dept Basic Sci Alexandria Egypt Arab Acad Sci Technol & Maritime Transport Coll Engn & Technol Dept Elect & Commun Engn Alexandria Egypt

Breast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG-16, are utilised and re-trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the support vector machine (SVM) classifier is utilised instead of the last fully connected layer. Our models performance are verified utilising k-fold cross-validation. Our proposed techniques are trained and evaluated on three mammographic datasets: mammographic image analysis society, digital database for screening mammography (DDSM) and the curated breast imaging subset of DDSM. This paper explains end-to-end fully convolutional neural networks without any prepossessing or post-processing. The proposed technique of employing ResNet50 hybridised with SVM achieves the best performance, specifically with the DDSM dataset, producing 97.98% accuracy, 98.46% area under the curve, 97.63% sensitivity, 96.51% precision, 95.97% F1 score and computational time 1.8934 s.

关键词： support vector machines pattern classification mammography medical image processing feature extraction image classification cancer learning (artificial intelligence) convolutional neural nets screening mammography curated breast imaging subset end-to-end fully convolutional neural networks ResNet50 SVM DDSM dataset computational time breast cancer classification framework deep learning low computational requirements digital database mammographic image analysis society mammographic datasets cross-validation models performance fully connected layer support vector machine classifier tagged data data augmentation transfer learning

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machine learning-based edge-computing on a multi-level architecture of WSN and IoT for real-time fall detection

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IET WIRELESS SENSOR SYSTEMS 2020年第6期10卷 320-332页

作者： El Attaoui, Amina Largo, Salma Kaissari, Soufiane Benba, Achraf Jilbab, Abdelilah Bourouhou, Abdennaser Mohammed V Univ ENSET STIS Elect Sensors Syst & Nanobiotechnol Rabat Morocco INPT Rabat STRS Lab Smart Embedded Enterprise & Distributed Syst SEED Rabat Morocco

Health telemonitoring systems are constrained by the computational and data transmission load resulting from the large volumes of various measured signals, e.g. in the fall detection application. Nevertheless, the trend of movement and the implementation of computer intelligence in intelligent devices ensure an intelligent and convenient method for continuous real-time telemonitoring of health conditions. In this paper, fall detection is presented while leveraging edge computing integrated on a multi-level architecture combines the Wireless Sensors Network and the Internet of Things. Particularly, we present a complete study and implementation scenarios while investigating the performances of machine learning algorithms to distinguish between different fall patterns and activities of daily living using a set of significant extracted features from measured acceleration and angular velocity signals. For low computational requirements and to improve the classification performances, the Linear Discriminant Analysis is used to reduce the dimensionality of extracted features. The experimental results assess the performances of the proposed approach in fall detection that show the highest accuracy of 99.92% provided using the KNN classifier and accuracy of 97.5% for fall pattern recognition using the SVM classifier. Also, the online classification on the Fog device reached an accuracy of 94.42% using the SVM classifier.

关键词： geriatrics pattern recognition support vector machines feature extraction pattern classification telemedicine patient monitoring wireless sensor networks learning (artificial intelligence) machine learning-based edge-computing multilevel architecture WSN IoT real-time fall detection health telemonitoring systems computational data transmission burden fall detection application computer intelligence implementation smart devices real-time monitoring health conditions human fall detection leveraging edge wireless sensor network implementation scenarios machine-learning algorithms different fall patterns significant extracted features measured acceleration angular velocity signals low computational requirements online computing classification performances supervised learning fall pattern recognition support vector machine classifier run-time classification Fog device

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Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images

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REMOTE SENSING 2021年第10期13卷 1960-1960页

作者： Sharma, Amit Hubert-Moy, Laurance Buvaneshwari, Sriramulu Sekhar, Muddu Ruiz, Laurent Moger, Hemanth Bandyopadhyay, Soumya Corgne, Samuel Univ Rennes Ctr Natl Rech Sci Unite Mixte Rech Littoral EnvironmGeomatTeledetect F-35043 Rennes France Indian Inst Sci Indo French Cell Water Sci Bangalore 560012 Karnataka India Indian Inst Sci Dept Civil Engn Bangalore 560012 Karnataka India Univ Toulouse III Paul Sabatier Ctr Natl Rech Sci Inst Rech Dev Unite Mixte Rech LittoralGeosci Environm F-31401 Toulouse France Inst Natl Rech Agr Alimentat & Environm Unite Mixte Rech Littoral Sol Agro & Hydrosyst Sp AgroCampus Ouest F-35043 Rennes France Indian Space Res Org Earth Observat & Disaster Management Programme Of Bangalore 560094 Karnataka India

Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study's main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calculating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014-2016 using ground observations. Kappa coefficients ranged from 0.81-0.96 for 2014-2015 and 0.62-0.89 for 2015-2016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6% to 16.5% of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions.

关键词： groundwater irrigation optical remote sensing NDVI support vector machine classifier Kabini critical zone observatory

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Novel climate classification based on the information of solar radiation intensity: An application to the climatic zoning of Morocco

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ENERGY CONVERSION AND MANAGEMENT 2021年 247卷 114770-114770页

作者： Anas, Hajou Youness, El Mghouchi Halima, Yakoubi Nawal, Abdou Mohamed, Chaoui Moulay Ismail Univ Fac Sci Meknes Meknes Morocco Moulay Ismail Univ ENSAM Meknes Morocco

Prior knowledge of solar radiation potential is a necessity for the decision making and the establishment of solar projects. Current prediction methods are site-dependent and their performance outside the area of application is questionable. Therefore, a solar radiation zoning method using a combination of supervised and unsupervised machine learning techniques and satellite gridded data is proposed. The method uses Agglomerative Hierarchical Clustering (AHC) following the ward's method based on the monthly mean solar insolation on a horizontal surface (H), and support vector machine classifier (SVM-C) to assist AHC to effectively classify all the areas based on solar radiation data plus temperature, humidity, wind speed and precipitation. Four solar radiation zones have been established. The zones are homogeneous in space and have distinctive solar and meteorological characteristics. The t-distributed Stochastic Neighbor Embedding (t-SNE) is applied to the obtained zones to distinguish trends within solar radiation zones based on temperature, humidity and wind speed, and a total of 8 sub-zones with distinctive meteorological characteristics have been identified. The resulting solar radiation zones and sub-zones cover all Morocco can help for preliminary assessment and decision making, especially for areas with no solar radiation records.

关键词： Solar radiation forecasting Solar radiation zoning Agglomerative hierarchical clustering support vector machine classifier Morocco

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LBP-based information assisted intelligent system for COVID-19 identification

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 134卷 104453-104453页

作者： Maheshwari, Shishir Sharma, Rishi Raj Kumar, Mohit Birla Inst Technol & Sci Discipline Elect & Elect Engn Pilani 333031 Rajasthan India Def Inst Adv Technol Dept Elect Engn Pune 411025 Maharashtra India Indian Inst Technol Kanpur NAF Dept Kanpur Uttar Pradesh India

A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.

关键词： COVID-19 Local binary pattern Entropy Gray level co-occurrence matrix support vector machine classifier Chest X-ray

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An efficient image segmentation and classification of lung lesions in pet and CT image fusion using DTWT incorporated SVM

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MICROPROCESSORS AND MICROSYSTEMS 2021年 82卷 103958-103958页

作者： Priya, R. Mohana Venkatesan, P. Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Elect & Commun Engn Kanchipuram Tamil Nadu India

Automatic segmentation and classification of fused lung Computed Tomography (CT) and Positron Emission Tomography (PET) images is presented. This system consists of four basic stages: 1). Lung image fusion process;2). Segmentation of fused lung CT/PET images;3). Post pre-processing;4). Classification of fused lung images. In the first step, the lung image fusion process is made by deep learning method. At first, the input CT/PET images are decomposed by Dual Tree m-band Wavelet Transform (DTWT). The coefficients of DTWT are fused by deep learning method. This fused image of CT/PET is the input for the following steps. In the next step, the fused CT/PET images are decomposed by DTWT. It produces lower and higher frequency sub-band coefficients. Then the lower frequency components are set to zero. Then higher frequency components are used for reconstruction. Then the clustering-based thresholding method is used for segmentation. In post pre-processing step, the unwanted small regions are removed by morphological operations. Then the lung region is detected. At last, in the classification step, the features are extracted by the intensity and texture-based features. These features are classified by hybrid classifiers like support vector machine (SVM) are used. The performance of the system has a higher classification accuracy of 99% using SVM classifier.

关键词： Computed tomography Positron emission tomography Fused lung image Dual tree m-band wavelet transform Thresholding support vector machine classifier

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Computer-aided skin cancer diagnosis based on a New meta-heuristic algorithm combined with support vector method

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102631-102631页

作者： Bi, Dezhong Zhu, Dongxia Sheykhahmad, Fatima Rashid Qiao, Mingqi Shandong Univ Tradit Chinese Med Inst Tradit Chinese Med 4655 Univ RdUniv Sci Pk Jinan 250355 Shandong Peoples R China Jinan City Peoples Hosp Jinan Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Skin cancer has grown significantly over the past decades, and the importance of its initial treatment is increasing day by day. This study aims to use a computer-aided diagnosis automatic system for the exact diagnosis of cancerous cases. In the present study, after applying pre-processing operations including contrast enhancement, image thresholding, and mathematical morphology for highlighting the important areas, feature extraction based on 20 different features has been applied. Then, for decreasing the system complexity, a new improved version of the world cup optimization algorithm is utilized for the features pruning. After optimal selection of the features, the injected into a support vector machine as a classifier to determine the cancerous areas. Experimental results are compared by different new algorithms including the classic SVM method, hybrid optimized neural networks based on gray wolf optimizer and particle swarm optimization algorithm, and also convolutional neural network. Results obtained for skin lesions images from two different databases indicate that the proposed method has higher efficiency than other published methods, demonstrating even better performance than the well-known classification method, the convolutional neural networks. The proposed method presents the highest correct detection rate - 92.64 for ACS images and 87.5 for ISIC images with very low false acceptance rates (4.41 % and 9 % for ACS and ISIC images, respectively) and even less false rejection rates, this is, 2.94 % on ACS images and 3.5 % on ISIC images. These results prove that the proposed method may be reliably used to diagnose skin cancer through dermoscopy images.

关键词： Computer-aided diagnosis Skin cancer Kapur image thresholding Feature extraction Feature classification support vector machine classifier Chaotic world cup optimization algorithm

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Diagnosis of myopathy, neuropathy using electromyogram signal and Wavelet coefficients 4

Diagnosis of myopathy, neuropathy using electromyogram signa...

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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Mousavi, Seyed Amirhos sein Hasan, Muntadher Ali Abdulrazzaq, Mustafa Hameed Naghavizadeh, Maryam Mashhad Branch Islamic Azad University Department of Biomedical Engineering Mashhad Iran Tehran Branch Islamic Azad University Department of Biomedical Engineering Tehran Iran

ISBN: (数字)9781728190907

ISBN: (纸本)9781728190907

The aim of this study was to diagnose and classify myopathy and neuropathy using Wavelet coefficients. In this study, EMG signals were obtained from 410 individuals using a needle electrode. The signal was decomposed to the second and third levels of wavelet using Daubechies mother wavelet. 13 features were extracted from each level and 3 optimal feature were selected using PSO and GA algorithms. The results show that the best classification accuracy was 93% using the PSO algorithm and level three Daubechies, and the best features that achieved this were the Mean, zero crossing, standard deviation. The results show that wavelet coefficients have been able to provide better resolution or in other words higher accuracy in diagnosing the disease. Given that neuromuscular diseases often have similar symptoms;the proposed method is useful in their diagnosis. © 2020 IEEE.

关键词： Feature extraction myopathy and neuropathy support vector machine classifier Wavelet coefficients

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