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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 10 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 107
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| Paper Title: | EMPOWERING LUNG CANCER PREDICTION: THE ENHANCED ARTIFICIAL BEE COLONY OPTIMIZATION (EABC) APPROACH |
| Authors Name: | Dr.A.Sathishkumar , Dr.P..Dhanasekaran |
| Download E-Certificate: | Download |
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IJRTI_200555
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| Published Paper Id: | IJRTI2502007 |
| Published In: | Volume 10 Issue 2, February-2025 |
| DOI: | http://doi.one/10.1729/Journal.43487 |
| Abstract: | The fundamental objective of lung cancer diagnosis is to streamline the testing procedures. It is accomplished by integrating a variety of techniques for performing image pre-processing, feature extraction, and classification utilising a bio-inspired methodology. To minimise The distortion in the unprocessed picture input, filtering is used. To discover the area of concern, segmentation is used. To learn more around the impacted sections, the feature selection is carried out. To locate the corresponding features on the needed image while maintaining suspicion, the bio-inspirational search method is used. The algorithms developed by this research will be very helpful to radiologists and doctors in identifying the areas of the lungs that are impacted by cancer. There are several tests that may be used to detect cancer in a lung image, and one of these methods is suggested to locate the cancer-affected area. Using image clustering based on image intensity, locate the areas of the lung pictures affected by cancer. The pre-processing, contrast enhancement, and segmentation processes used in this work. When the detection is not made at the proper moment, the problem's complexity rises. Due of the aforementioned circumstance, lung cancer monitoring systems typically employ the Enhanced Artificial Bee Colony Optimization (EABC) technique, which describes how the target's picture may vary for many potential solutions of the object. This study addresses the issues of object detection, noise reduction, segmentation, feature extraction, and calculating the accuracy level. |
| Keywords: | Enhanced Artificial Bee Colony Optimization (EABC), Marker-Controlled Watershed, Artificial Neural Networks |
| Cite Article: | "EMPOWERING LUNG CANCER PREDICTION: THE ENHANCED ARTIFICIAL BEE COLONY OPTIMIZATION (EABC) APPROACH", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 2, page no.a39-a48, February-2025, Available :http://www.ijrti.org/papers/IJRTI2502007.pdf |
| Downloads: | 000158 |
| 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 |
| Publication Details: |
Published Paper ID: IJRTI2502007
Registration ID:200555
Published In: Volume 10 Issue 2, February-2025
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.43487
Page No: a39-a48 Country: Perundurai, Erode, Tamilnadu, India Research Area: Science Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2502007 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2502007 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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