https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Lung Tumor Image Segmentation from Computer Tomography Images Using MobileNetV2 and Transfer Learning
Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China.
Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-4841-2488
Center for Eye & Vision Research, 17W Science Park, Hong Kong SAR, China.
Show others and affiliations
2023 (English)In: Bioengineering, E-ISSN 2306-5354, Vol. 10, no 8, article id 981Article in journal (Refereed) Published
Abstract [en]

Background: Lung cancer is one of the most fatal cancers worldwide, and malignant tumors are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms of lung cancer do not appear until it is already at an advanced stage. The proper segmentation of cancerous lesions in CT images is the primary method of detection towards achieving a completely automated diagnostic system. Method: In this work, we developed an improved hybrid neural network via the fusion of two architectures, MobileNetV2 and UNET, for the semantic segmentation of malignant lung tumors from CT images. The transfer learning technique was employed and the pre-trained MobileNetV2 was utilized as an encoder of a conventional UNET model for feature extraction. The proposed network is an efficient segmentation approach that performs lightweight filtering to reduce computation and pointwise convolution for building more features. Skip connections were established with the Relu activation function for improving model convergence to connect the encoder layers of MobileNetv2 to decoder layers in UNET that allow the concatenation of feature maps with different resolutions from the encoder to decoder. Furthermore, the model was trained and fine-tuned on the training dataset acquired from the Medical Segmentation Decathlon (MSD) 2018 Challenge. Results: The proposed network was tested and evaluated on 25% of the dataset obtained from the MSD, and it achieved a dice score of 0.8793, recall of 0.8602 and precision of 0.93. It is pertinent to mention that our technique outperforms the current available networks, which have several phases of training and testing.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI) , 2023. Vol. 10, no 8, article id 981
Keywords [en]
CT, deep learning, lung cancer, medical imaging, MobileNetV2, pulmonary nodule, UNET
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:mdh:diva-64171DOI: 10.3390/bioengineering10080981ISI: 001057600300001Scopus ID: 2-s2.0-85169124977OAI: oai:DiVA.org:mdh-64171DiVA, id: diva2:1794863
Available from: 2023-09-06 Created: 2023-09-06 Last updated: 2023-12-04Bibliographically approved

Open Access in DiVA

fulltext(4331 kB)7 downloads
File information
File name FULLTEXT01.pdfFile size 4331 kBChecksum SHA-512
cfe880bddf329da907b48938ac53a4fecbeb985367b72400ac83a173b4b59a4ec668c0e2063e9a1cd4867633d0a0e9930a217e621fc060a5b5e27a9d7a90ecbf
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Abdullah, Saad

Search in DiVA

By author/editor
Abdullah, Saad
By organisation
Embedded Systems
In the same journal
Bioengineering
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 7 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 9 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf