8 research outputs found
Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography
Atypical body temperature values can be an indication of abnormal physiological processes
associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging
modality capable of capturing the natural thermal radiation emitted by the skin surface, which is
connected to physiology-related pathological states. The implementation of artificial intelligence
(AI) methods for interpretation of thermal data can be an interesting solution to supply a second
opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to
perform a systematic review and meta-analysis concerning different biomedical thermal applications
in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a
qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation
of IRT imaging with AI, but more work is needed to retrieve significant features and improve
classification metrics.info:eu-repo/semantics/publishedVersio
thermogram Breast Cancer Detection : a comparative study of two machine learning techniques
Breast cancer is considered one of the major threats for womenâs health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%
The Encyclopedia of Neutrosophic Researchers - vol. 1
This is the first volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editorâs invitation. The authors are listed alphabetically. The introduction contains a short history of neutrosophics, together with links to the main papers and books. Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements
A model for the detection of breast cancer using machine learning and thermal images in a mobile environment
Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model
A model for the detection of breast cancer using machine learning and thermal images in a mobile environment
Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model
Collected Papers (on various scientific topics), Volume XII
This twelfth volume of Collected Papers includes 86 papers comprising 976 pages on Neutrosophics Theory and Applications, published between 2013-2021 in the international journal and book series âNeutrosophic Sets and Systemsâ by the author alone or in collaboration with the following 112 co-authors (alphabetically ordered) from 21 countries: Abdel Nasser H. Zaied, Muhammad Akram, Bobin Albert, S. A. Alblowi, S. Anitha, Guennoun Asmae, Assia Bakali, Ayman M. Manie, Abdul Sami Awan, Azeddine Elhassouny, Erick GonzĂĄlez-Caballero, D. Dafik, Mithun Datta, Arindam Dey, Mamouni Dhar, Christopher Dyer, Nur Ain Ebas, Mohamed Eisa, Ahmed K. Essa, Faruk Karaaslan, JoĂŁo Alcione Sganderla Figueiredo, Jorge Fernando Goyes GarcĂa, N. Ramila Gandhi, Sudipta Gayen, Gustavo Alvarez GĂłmez, Sharon Dinarza Ălvarez GĂłmez, Haitham A. El-Ghareeb, Hamiden Abd El-Wahed Khalifa, Masooma Raza Hashmi, Ibrahim M. Hezam, German Acurio Hidalgo, Le Hoang Son, R. Jahir Hussain, S. Satham Hussain, Ali Hussein Mahmood Al-Obaidi, Hays Hatem Imran, Nabeela Ishfaq, Saeid Jafari, R. Jansi, V. Jeyanthi, M. Jeyaraman, Sripati Jha, Jun Ye, W.B. Vasantha Kandasamy, Abdullah Kargın, J. Kavikumar, Kawther Fawzi Hamza Alhasan, Huda E. Khalid, Neha Andalleb Khalid, Mohsin Khalid, Madad Khan, D. Koley, Valeri Kroumov, Manoranjan Kumar Singh, Pavan Kumar, Prem Kumar Singh, Ranjan Kumar, Malayalan Lathamaheswari, A.N. Mangayarkkarasi, Carlos Rosero MartĂnez, Marvelio Alfaro Matos, Mai Mohamed, Nivetha Martin, Mohamed Abdel-Basset, Mohamed Talea, K. Mohana, Muhammad Irfan Ahamad, Rana Muhammad Zulqarnain, Muhammad Riaz, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Muhammad Zeeshan, Anjan Mukherjee, Mumtaz Ali, Deivanayagampillai Nagarajan, Iqra Nawaz, Munazza Naz, Roan Thi Ngan, Necati Olgun, Rodolfo GonzĂĄlez Ortega, P. Pandiammal, I. Pradeepa, R. Princy, Marcos David Oviedo RodrĂguez, JesĂșs Estupiñån Ricardo, A. Rohini, Sabu Sebastian, Abhijit Saha, Mehmet Èahin, Said Broumi, Saima Anis, A.A. Salama, Ganeshsree Selvachandran, Seyed Ahmad Edalatpanah, Sajana Shaik, Soufiane Idbrahim, S. Sowndrarajan, Mohamed Talea, Ruipu Tan, Chalapathi Tekuri, Selçuk Topal, S. P. Tiwari, Vakkas Uluçay, Maikel Leyva VĂĄzquez, Chinnadurai Veerappan, M. Venkatachalam, Luige VlÄdÄreanu, Ćtefan VlÄduĆŁescu, Young Bae Jun, Wadei F. Al-Omeri, Xiao Long Xin.âŹâŹâŹâŹâŹ
Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm
The early detection of breast cancer makes many
women survive. In this paper, a CAD system classifying breast
cancer thermograms to normal and abnormal is proposed. This
approach consists of two main phases: automatic segmentation
and classification. For the former phase, an improved segmentation
approach based on both Neutrosophic sets (NS) and
optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed.
Also, post-segmentation process was suggested to segment
breast parenchyma (i.e. ROI) from thermogram images. For the
classification, different kernel functions of the Support Vector
Machine (SVM) were used to classify breast parenchyma into
normal or abnormal cases. Using benchmark database, the
proposed CAD system was evaluated based on precision, recall,
and accuracy as well as a comparison with related work. The
experimental results showed that our system would be a very
promising step toward automatic diagnosis of breast cancer
using thermograms as the accuracy reached 100%