Intelligent health model for medical imaging to guide laymen using neural cellular automata
Abstract A layman in health systems is a person who doesn’t have any knowledge about health data i.e., X-ray, MRI, CT scan, and health examination reports, etc. The motivation behind the proposed invention is to help laymen to make medical images understandable. The health model is trained using a n...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94032-y |
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| author | Sandeep Kumar Sharma Chiranji Lal Chowdhary Vijay Shankar Sharma Adil Rasool Arfat Ahmad Khan |
| author_facet | Sandeep Kumar Sharma Chiranji Lal Chowdhary Vijay Shankar Sharma Adil Rasool Arfat Ahmad Khan |
| author_sort | Sandeep Kumar Sharma |
| collection | DOAJ |
| description | Abstract A layman in health systems is a person who doesn’t have any knowledge about health data i.e., X-ray, MRI, CT scan, and health examination reports, etc. The motivation behind the proposed invention is to help laymen to make medical images understandable. The health model is trained using a neural network approach that analyses user health examination data; predicts the type and level of the disease and advises precaution to the user. Cellular Automata (CA) technology has been integrated with the neural networks to segment the medical image. The CA analyzes the medical images pixel by pixel and generates a robust threshold value which helps to efficiently segment the image and identify accurate abnormal spots from the medical image. The proposed method has been trained and experimented using 10000+ medical images which are taken from various open datasets. Various text analysis measures i.e., BLEU, ROUGE, and WER are used in the research to validate the produced report. The BLEU and ROUGE calculate a similarity to decide how the generated text report is closer to the original report. The BLEU and ROUGE scores of the experimented images are approximately 0.62 and 0.90, claims that the produced report is very close to the original report. The WER score 0.14, claims that the generated report contains the most relevant words. The overall summary of the proposed research is that it provides a fruitful medical report with accurate disease and precautions to the laymen. |
| format | Article |
| id | doaj-art-827faf9b83484347aed081eb72bfa810 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-827faf9b83484347aed081eb72bfa8102025-08-20T03:08:40ZengNature PortfolioScientific Reports2045-23222025-05-0115111910.1038/s41598-025-94032-yIntelligent health model for medical imaging to guide laymen using neural cellular automataSandeep Kumar Sharma0Chiranji Lal Chowdhary1Vijay Shankar Sharma2Adil Rasool3Arfat Ahmad Khan4Department of Computer and Communication Engineering, Manipal University JaipurSchool of Computer Science Engineering and Information Systems, Vellore Institute of TechnologyDepartment of Computer and Communication Engineering, Manipal University JaipurDepartment of Computer, Bakhtar UniversityDepartment of Computer Science, College of Computing, Khon Kaen UniversityAbstract A layman in health systems is a person who doesn’t have any knowledge about health data i.e., X-ray, MRI, CT scan, and health examination reports, etc. The motivation behind the proposed invention is to help laymen to make medical images understandable. The health model is trained using a neural network approach that analyses user health examination data; predicts the type and level of the disease and advises precaution to the user. Cellular Automata (CA) technology has been integrated with the neural networks to segment the medical image. The CA analyzes the medical images pixel by pixel and generates a robust threshold value which helps to efficiently segment the image and identify accurate abnormal spots from the medical image. The proposed method has been trained and experimented using 10000+ medical images which are taken from various open datasets. Various text analysis measures i.e., BLEU, ROUGE, and WER are used in the research to validate the produced report. The BLEU and ROUGE calculate a similarity to decide how the generated text report is closer to the original report. The BLEU and ROUGE scores of the experimented images are approximately 0.62 and 0.90, claims that the produced report is very close to the original report. The WER score 0.14, claims that the generated report contains the most relevant words. The overall summary of the proposed research is that it provides a fruitful medical report with accurate disease and precautions to the laymen.https://doi.org/10.1038/s41598-025-94032-yMedical imageLaymanHealthcareMachine learningCellular automata |
| spellingShingle | Sandeep Kumar Sharma Chiranji Lal Chowdhary Vijay Shankar Sharma Adil Rasool Arfat Ahmad Khan Intelligent health model for medical imaging to guide laymen using neural cellular automata Scientific Reports Medical image Layman Healthcare Machine learning Cellular automata |
| title | Intelligent health model for medical imaging to guide laymen using neural cellular automata |
| title_full | Intelligent health model for medical imaging to guide laymen using neural cellular automata |
| title_fullStr | Intelligent health model for medical imaging to guide laymen using neural cellular automata |
| title_full_unstemmed | Intelligent health model for medical imaging to guide laymen using neural cellular automata |
| title_short | Intelligent health model for medical imaging to guide laymen using neural cellular automata |
| title_sort | intelligent health model for medical imaging to guide laymen using neural cellular automata |
| topic | Medical image Layman Healthcare Machine learning Cellular automata |
| url | https://doi.org/10.1038/s41598-025-94032-y |
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