التفاصيل البيبلوغرافية
| العنوان: |
Detecting tumors in medical images using segmentation and feature extraction techniques |
| المؤلفون: |
A. Sinduja, H. Benjamin Fredrick David, C. Sathiya Kumar, S.P. Raja |
| المصدر: |
e-Prime: Advances in Electrical Engineering, Electronics and Energy, Vol 9, Iss , Pp 100614- (2024) |
| بيانات النشر: |
Elsevier, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Medical images, Segmentation, Feature extraction, Feature selection, Classification, Tumor detection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Tumors rank among the most prevalent and lethal diseases worldwide, emphasizing the paramount importance of early detection in mitigating mortality rates. There has been a tremendous growth in medical image segmentation and classification for diagnosing cancer. This led to the various developments in medical research that help in diagnosing types of cancers and other human medical problems. This work focuses on performing effective segmentation procedures and feature extraction by a proposed hybrid intelligent technique for detecting tumors in the medical images. Additionally, the research addresses the design aspects of feature extraction, image classification, and texture features. These features are reduced by employing feature selection. Finally, experiments were conducted to compare tumor detection in medical images using the proposed algorithm with other existing algorithms. The results demonstrated promising achievements in terms of their evaluation metrics. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2772-6711 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2772671124001943; https://doaj.org/toc/2772-6711 |
| DOI: |
10.1016/j.prime.2024.100614 |
| URL الوصول: |
https://doaj.org/article/14bad7d4a7984314abbc6fd1fbe8b60b |
| رقم الانضمام: |
edsdoj.14bad7d4a7984314abbc6fd1fbe8b60b |
| قاعدة البيانات: |
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