IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT
In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to d...
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000494 |
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| author | Jayapradha J Su-Cheng Haw Naveen Palanichamy Kok-Why Ng Senthil Kumar Thillaigovindhan |
| author_facet | Jayapradha J Su-Cheng Haw Naveen Palanichamy Kok-Why Ng Senthil Kumar Thillaigovindhan |
| author_sort | Jayapradha J |
| collection | DOAJ |
| description | In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT). The model integrates two architectures, MobileNetV2 and U-NET, for classifying the input lung data. The input CT lung images are pre-processed using Z-score Normalization. The semantic features of lung images are extracted based on texture, intensity, and shape to provide information to the training network. • In this work, the transfer learning technique is incorporated, and the pre-trained NN was used as an encoder for the U-NET model for segmentation. Furthermore, Support Vector Machine is used here to classify input lung data as benign and malignant. • The results are measured based on the metrics such as, specificity, sensitivity, precision, accuracy and F-Score, using the data from benchmark datasets. Compared to the existing lung tumor segmentation and classification models, the proposed model provides better results and evidence for earlier disease diagnosis. |
| format | Article |
| id | doaj-art-d853337eadac44169b872fb62edd93ab |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-d853337eadac44169b872fb62edd93ab2025-02-12T05:31:09ZengElsevierMethodsX2215-01612025-06-0114103201IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMTJayapradha J0Su-Cheng Haw1Naveen Palanichamy2Kok-Why Ng3Senthil Kumar Thillaigovindhan4Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India; Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, MalaysiaFaculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia; Corresponding author.Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, MalaysiaFaculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, MalaysiaDepartment of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, IndiaIn recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT). The model integrates two architectures, MobileNetV2 and U-NET, for classifying the input lung data. The input CT lung images are pre-processed using Z-score Normalization. The semantic features of lung images are extracted based on texture, intensity, and shape to provide information to the training network. • In this work, the transfer learning technique is incorporated, and the pre-trained NN was used as an encoder for the U-NET model for segmentation. Furthermore, Support Vector Machine is used here to classify input lung data as benign and malignant. • The results are measured based on the metrics such as, specificity, sensitivity, precision, accuracy and F-Score, using the data from benchmark datasets. Compared to the existing lung tumor segmentation and classification models, the proposed model provides better results and evidence for earlier disease diagnosis.http://www.sciencedirect.com/science/article/pii/S2215016125000494Integrated Model (IM- LTS) for Lung Tumor Segmentation] |
| spellingShingle | Jayapradha J Su-Cheng Haw Naveen Palanichamy Kok-Why Ng Senthil Kumar Thillaigovindhan IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT MethodsX Integrated Model (IM- LTS) for Lung Tumor Segmentation] |
| title | IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT |
| title_full | IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT |
| title_fullStr | IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT |
| title_full_unstemmed | IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT |
| title_short | IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT |
| title_sort | im lts an integrated model for lung tumor segmentation using neural networks and iomt |
| topic | Integrated Model (IM- LTS) for Lung Tumor Segmentation] |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000494 |
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