Citation

Jayaram, Jayapradha and Haw, Su Cheng and Palanichamy, Naveen and Ng, Kok Why and Thillaigovindhan, Senthil Kumar (2025) IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT. MethodsX, 14. p. 103201. ISSN 22150161

Text IM- LTS_ An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

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 integratestwo architectures, MobileNetV2 and U-NET, for classifying the input lung data. The input CT lung images are preprocessed 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.

Item Type:	Article
Uncontrolled Keywords:	Neural networks, support vector machine
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Mar 2025 01:59
Last Modified:	06 Mar 2025 01:59
URII:	http://shdl.mmu.edu.my/id/eprint/13589

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