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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
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21686
Article Published : 8549
Total Authors : 22487
Total Reviewer : 811
Total Countries : 159
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| Paper Title: | AI-Based liver abnormalities detection using medical images |
| Authors Name: | Ms Jaishma Kumari B , Ms Nisha Roche , Ms Pruthvi M R , Ms Prajna M , Dr Saumya Y M |
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IJRTI_204466
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| Published Paper Id: | IJRTI2505323 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | This paper introduces a deep-learning framework specifically designed for the accurate detection of hepatomegaly in CT images. This approach aims to address the limitations found in conventional imaging modalities and manual assessment methods. A meticulously curated and diverse dataset, including annotated CT scans of both normal and hepatomegaly-affected livers, is employed to support robust model training and optimization. Extensive fine-tuning of the model architecture and hyperparameters is conducted to enhance generalization across varied datasets and clinical scenarios. Ethical considerations are fundamental to this methodology, emphasizing transparency, fairness, and patient privacy throughout the model's development. Successful implementation of this framework is expected to significantly improve the efficiency of hepatomegaly detection in clinical settings, leading to better patient care. Additionally, the research highlights a commitment to advancing medical imaging technology while maintaining ethical standards and prioritizing patient welfare. The proposed deep-learning model's potential impact goes beyond hepatomegaly detection, with broader implications for the field of medical imaging and precision medicine. By revolutionizing diagnostic processes, this work aims to enhance healthcare outcomes and pave the way for future innovations in this critical area. |
| Keywords: | Dice Similarity Coefficient (DSC), Convolutional Neural Networks (CNNs), Multiple Instance Learning (MIL),Computed Tomography (CT), |
| Cite Article: | "AI-Based liver abnormalities detection using medical images ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.d161-d166, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505323.pdf |
| Downloads: | 000381 |
| 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: IJRTI2505323
Registration ID:204466
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: d161-d166 Country: mangalore, karnataka, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505323 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505323 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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