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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Issue: June 2026
Volume 11 | Issue 6
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 121
Article Submitted : 24737
Article Published : 9361
Total Authors : 24875
Total Reviewer : 861
Total Countries : 169
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| Paper Title: | Development of Medical Image Segmentation Model for Tumor Localization in CT Scan Images using U-Net Architecture and Transfer Learning |
| Authors Name: | Manigandan R , Jansi S , Roja D , Shalini A |
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IJRTI_212302
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| Published Paper Id: | IJRTI2605002 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | Abstract—Medical image segmentation plays a vital role in early detection and diagnosis of tumors from CT scan images. Traditional methods rely heavily on manual analysis by radiologists, which is time-consuming and prone to human error. This project proposes an automated tumor localization system using deep learning techniques, specifically the U-Net architecture combined with transfer learning. The model is trained on CT scan datasets to accurately segment tumor regions. Pre-trained encoder networks improve feature extraction and reduce training time. The proposed system enhances segmentation accuracy, reduces manual effort, and supports clinical decision-making. Experimental results demonstrate improved performance in terms of Dice coefficient and Intersection over Union (IoU), making the model effective for medical applications. |
| Keywords: | Keywords— Medical Image Segmentation, U-Net, Transfer Learning, CT Scan, Tumor Detection, Deep Learning |
| Cite Article: | " Development of Medical Image Segmentation Model for Tumor Localization in CT Scan Images using U-Net Architecture and Transfer Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a19-a24, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605002.pdf |
| Downloads: | 000205548 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605002
Registration ID:212302
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: a19-a24 Country: Tiruvannamalai, Tamil Nadu, India Research Area: Medical Science Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605002 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605002 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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