Pub Date : 2023-01-01DOI: 10.1504/ijbet.2023.10058325
J. Saini, Sanjeev Kumar, N.A. Seema
{"title":"Master slave configuration in robotic surgery through image processing","authors":"J. Saini, Sanjeev Kumar, N.A. Seema","doi":"10.1504/ijbet.2023.10058325","DOIUrl":"https://doi.org/10.1504/ijbet.2023.10058325","url":null,"abstract":"","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"42 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66808052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbet.2023.133797
M. Vijaya Madhavi, T. Christy Bobby
Breast tissue density is one of the significant risk-marker for identification of breast cancer in early stage. In the proposed work, fibro-glandular region is explored and classification of breast density as dense and non-dense is performed. Image pre-processing is performed to improve the image quality followed by segmentation of breast region to obtain region of interest (RoI). For the obtained RoI, pseudo colouring is performed to improve image acuity accompanied by R-image extraction and post-processing to obtain fibro-glandular breast tissues. Area, histogram, fractal, grey-level co-occurrence matrix and grey-level run length matrix features are derived from both fibro-glandular and RoI regions and ratiometric value of features are computed. Further, mutual-information-based feature ranking algorithm is applied on the derived ratiometric values and the significant features are identified. These significant features when fed to least square-support vector machine produced average classification accuracy (%) of 86.1 ± 6.03 for mini-MIAS and 82.3 ± 4.78 for CBIS-DDSM database.
{"title":"Exploration of fibro-glandular region and breast density classification of digitised mammograms using least square support vector machine","authors":"M. Vijaya Madhavi, T. Christy Bobby","doi":"10.1504/ijbet.2023.133797","DOIUrl":"https://doi.org/10.1504/ijbet.2023.133797","url":null,"abstract":"Breast tissue density is one of the significant risk-marker for identification of breast cancer in early stage. In the proposed work, fibro-glandular region is explored and classification of breast density as dense and non-dense is performed. Image pre-processing is performed to improve the image quality followed by segmentation of breast region to obtain region of interest (RoI). For the obtained RoI, pseudo colouring is performed to improve image acuity accompanied by R-image extraction and post-processing to obtain fibro-glandular breast tissues. Area, histogram, fractal, grey-level co-occurrence matrix and grey-level run length matrix features are derived from both fibro-glandular and RoI regions and ratiometric value of features are computed. Further, mutual-information-based feature ranking algorithm is applied on the derived ratiometric values and the significant features are identified. These significant features when fed to least square-support vector machine produced average classification accuracy (%) of 86.1 ± 6.03 for mini-MIAS and 82.3 ± 4.78 for CBIS-DDSM database.","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135952861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbet.2023.133791
Nagavali Saka, S. Murali Krishna
Nowadays, different thyroid disorders are observed which are affecting the human population worldwide. Hence, to provide suitable treatment and be cost-consuming for the patients, an earlier diagnosis is required. To improve prediction, this paper proposed Bayes-linear discriminant analysis (B-LDA) and cuckoo search based weighted decision tree (CSWDT) models to predict the autoimmune thyroid risk assessment from the obtained dataset. Initially, after pre-processing, the features are extracted using the deep MLP model, and the significant features are fused by using the B-LDA model which overcomes the dimensionality reduction issue. Further, the classification is performed by using the optimised cuckoo search with a weighted decision tree model. In addition, K-fold cross-validation is performed and attains a better accuracy value of 99.5% in thyroid disease prediction.
{"title":"BLDA-CSWDT autoimmune thyroid disease risks predictive model using machine learning and deep feature extraction techniques","authors":"Nagavali Saka, S. Murali Krishna","doi":"10.1504/ijbet.2023.133791","DOIUrl":"https://doi.org/10.1504/ijbet.2023.133791","url":null,"abstract":"Nowadays, different thyroid disorders are observed which are affecting the human population worldwide. Hence, to provide suitable treatment and be cost-consuming for the patients, an earlier diagnosis is required. To improve prediction, this paper proposed Bayes-linear discriminant analysis (B-LDA) and cuckoo search based weighted decision tree (CSWDT) models to predict the autoimmune thyroid risk assessment from the obtained dataset. Initially, after pre-processing, the features are extracted using the deep MLP model, and the significant features are fused by using the B-LDA model which overcomes the dimensionality reduction issue. Further, the classification is performed by using the optimised cuckoo search with a weighted decision tree model. In addition, K-fold cross-validation is performed and attains a better accuracy value of 99.5% in thyroid disease prediction.","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135953697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijbet.2023.134600
Bhanupriya Mishra, Neelamshobha Nirala
Type-2 diabetes mellitus (T2DM) is a lifelong metabolic disease with worldwide prevalence. It can drastically decrease the life expectancy of any subject with a huge economic burden. The present study aimed to create a non-invasive and economical tool for automatic detection of T2DM using electrocardiogram (ECG) signals. The flexible analytic wavelet transform is used to evaluate the ECG by decomposing it into predictable sub-bands. Statistical and time-domain features were extracted from each sub-band. Different feature selection techniques were applied to obtain the most relevant features. The top nine features, selected by using the one-R attribute eval feature selection technique, were fed into the various types of machine learning classifiers. In tested classifiers, the fine k-nearest neighbour and optimisable KNN classifiers have shown the highest average accuracy of 94.94% and 94.61% respectively. The results suggest that the proposed approach provides an efficient non-invasive T2DM detection method in regular applications.
{"title":"Identification of type-2 diabetes by electrocardiogram signal using flexible analytical wavelet transform","authors":"Bhanupriya Mishra, Neelamshobha Nirala","doi":"10.1504/ijbet.2023.134600","DOIUrl":"https://doi.org/10.1504/ijbet.2023.134600","url":null,"abstract":"Type-2 diabetes mellitus (T2DM) is a lifelong metabolic disease with worldwide prevalence. It can drastically decrease the life expectancy of any subject with a huge economic burden. The present study aimed to create a non-invasive and economical tool for automatic detection of T2DM using electrocardiogram (ECG) signals. The flexible analytic wavelet transform is used to evaluate the ECG by decomposing it into predictable sub-bands. Statistical and time-domain features were extracted from each sub-band. Different feature selection techniques were applied to obtain the most relevant features. The top nine features, selected by using the one-R attribute eval feature selection technique, were fed into the various types of machine learning classifiers. In tested classifiers, the fine k-nearest neighbour and optimisable KNN classifiers have shown the highest average accuracy of 94.94% and 94.61% respectively. The results suggest that the proposed approach provides an efficient non-invasive T2DM detection method in regular applications.","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1504/ijbet.2022.10045022
T. Kalaiselvi, P. Sriramakrishnan
{"title":"Magnetic resonance brain volume property-based accelerate medical image algorithms using graphics processing unit","authors":"T. Kalaiselvi, P. Sriramakrishnan","doi":"10.1504/ijbet.2022.10045022","DOIUrl":"https://doi.org/10.1504/ijbet.2022.10045022","url":null,"abstract":"","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66805803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1504/ijbet.2022.10045073
Sivasankar Arumugam, T. Ravi, R. Ranganathan
{"title":"Need for customisation in preventing pressure ulcers for wheelchair patients - a load distribution approach","authors":"Sivasankar Arumugam, T. Ravi, R. Ranganathan","doi":"10.1504/ijbet.2022.10045073","DOIUrl":"https://doi.org/10.1504/ijbet.2022.10045073","url":null,"abstract":"","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66806019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1504/ijbet.2022.10049495
P. Sriramakrishnan, T. Kalaiselvi, P. Kumarashankar
{"title":"Machine learning approach for automatic brain tumour detection using patch-based feature extraction and classification","authors":"P. Sriramakrishnan, T. Kalaiselvi, P. Kumarashankar","doi":"10.1504/ijbet.2022.10049495","DOIUrl":"https://doi.org/10.1504/ijbet.2022.10049495","url":null,"abstract":"","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66806196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1504/ijbet.2022.10050023
S. Talbar, A. Handique, Prasad Dutande, Ujjwal Baid, Sudip Paul, G. K. Mourya
{"title":"Modified U-Net for fully automatic liver segmentation from abdominal CT-image","authors":"S. Talbar, A. Handique, Prasad Dutande, Ujjwal Baid, Sudip Paul, G. K. Mourya","doi":"10.1504/ijbet.2022.10050023","DOIUrl":"https://doi.org/10.1504/ijbet.2022.10050023","url":null,"abstract":"","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66806285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1504/ijbet.2022.10050025
Sarina Mansor, K. Teoh, L. Looi, J. T. H. Lee, S. Y. Khor, M. F. A. Fauzi, Afiqah Abu Samah
{"title":"Mitotic cells detection in H&E-stained breast carcinoma images","authors":"Sarina Mansor, K. Teoh, L. Looi, J. T. H. Lee, S. Y. Khor, M. F. A. Fauzi, Afiqah Abu Samah","doi":"10.1504/ijbet.2022.10050025","DOIUrl":"https://doi.org/10.1504/ijbet.2022.10050025","url":null,"abstract":"","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66806353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1504/ijbet.2022.10050145
S. S. Kamath, K. Karthik
{"title":"Swarm optimisation-based bag of visual words model for content-based X-ray scan retrieval","authors":"S. S. Kamath, K. Karthik","doi":"10.1504/ijbet.2022.10050145","DOIUrl":"https://doi.org/10.1504/ijbet.2022.10050145","url":null,"abstract":"","PeriodicalId":51752,"journal":{"name":"International Journal of Biomedical Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66806571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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