Canker and Cold Sore Classification Using Xeception Technique

Especially common in nations of low and moderate-income, mouth disease was responsible for 177,384 fatalities worldwide in 2018. A cold sore is mild blistering of the lips or mouth area. Herpes simplex virus causes them, and they go away on their own in 7-10 days. Generally, the first three to four days of a canker sore are the most excruciating. Within 6-24 hours, the ulcer changes from a red, inflamed patch to a slight, circular depression of 3-9 mm in diameter. Canker sores cause tingling or burning before they become noticeable, but the pain subsides, and the sore heals in 10-14 days, generally without scarring. If canker and cold sores in the mouth could be automatically identified, early and cheap diagnosis of the condition could be achieved. Canker sores and cold sores are only two of the many oral disorders that can be detected and diagnosed using modern digital technologies. Diagnosing oral illnesses with deep learning is challenging. In this research, we used two classes, canker sores and cold sores, to build a novel technique. A new dataset, dubbed “Mouth Disease” (MD), has been created, and it splits diseases into two groups. An application of the Xception model is used to categorise the illness. Compared to previous approaches, the suggested Xception model showed superior performance, with an accuracy of 99.60%.

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Publication Details

Journal
Journal of Machine Learning and Deep Learning
Published
2025-06-30
DOI
https://doi.org/10.64820/aepjmldl.21.18.24.62025
Citations
1
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
1.21
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article

Canker and Cold Sore Classification Using Xeception Technique

Maham Ali, Bilal Shabbir Qaisar, Muhammad Haseeb Aslam, M. Mudasar Azeem et al.
1 citations
Journal of Machine Learning and Deep Learning
Advanced Algorithms and Applications
1.21
article

Canker and Cold Sore Classification Using Xeception Technique

Maham Ali, Bilal Shabbir Qaisar, Muhammad Haseeb Aslam, M. Mudasar Azeem, Muhammad Talha Hashmi
article en
1 citations

Abstract

Especially common in nations of low and moderate-income, mouth disease was responsible for 177,384 fatalities worldwide in 2018. A cold sore is mild blistering of the lips or mouth area. Herpes simplex virus causes them, and they go away on their own in 7-10 days. Generally, the first three to four days of a canker sore are the most excruciating. Within 6-24 hours, the ulcer changes from a red, inflamed patch to a slight, circular depression of 3-9 mm in diameter. Canker sores cause tingling or burning before they become noticeable, but the pain subsides, and the sore heals in 10-14 days, generally without scarring. If canker and cold sores in the mouth could be automatically identified, early and cheap diagnosis of the condition could be achieved. Canker sores and cold sores are only two of the many oral disorders that can be detected and diagnosed using modern digital technologies. Diagnosing oral illnesses with deep learning is challenging. In this research, we used two classes, canker sores and cold sores, to build a novel technique. A new dataset, dubbed “Mouth Disease” (MD), has been created, and it splits diseases into two groups. An application of the Xception model is used to categorise the illness. Compared to previous approaches, the suggested Xception model showed superior performance, with an accuracy of 99.60%.

Journal of Machine Learning and Deep LearningVol. 2(1)
Openalex Percentile: Top 17%
Advanced Algorithms and Applications
1.21
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