Objectives: To detect and distinguish oral malignant and non-malignant lesions from clinical
photographs using YOLO v8 deep learning algorithm.
Methods: This is a diagnostic study conducted using clinical images of oral cavity lesions. The
427 clinical images of the oral cavity were extracted from a publicly available dataset repository
specifically Kaggle and Mendeley data repositories. The datasets obtained were then categorized
into normal, abnormal (non-malignant), and malignant oral lesions by two independent oral
pathologists using Roboflow Annotation Software. The images collected were first set to a
resolution of 640 x 640 pixels and then randomly split into 3 sets: training, validation, and testing
– 70:20:10, respectively. Finally, the image classification analysis was performed using the YOLO
V8 classification algorithm at 20 epochs to classify and distinguish between malignant lesions,
non-malignant lesions, and normal tissue. The performance of the algorithm was assessed using
the following parameters accuracy, precision, sensitivity, and specificity.
Results: After training and validation with 20 epochs, our oral cancer image classification
algorithm showed maximum performance at 15 epochs. Based on the generated normalized
confusion matrix, the sensitivity of our algorithm in classifying normal images, non-malignant
images, and malignant images was 71%, 47%, and 54%, respectively. The specificity of our
algorithm in classifying normal images, non-malignant, and malignant images were 86%, 65%,
and 72%. The precision of our algorithm in classifying normal images, non-malignant images,
and malignant images was 73%, 62%, and 35%, respectively. The overall accuracy of our oral
cancer image classification algorithm was 55%. On a test set, our algorithm gave an overall 96%
accuracy in detecting malignant lesions.
Conclusion: Our object classification algorithm showed a promising application in
distinguishing between malignant, non-malignant, and normal tissue. Further studies and
continued research will observe increasing emphasis on the use of artificial intelligence to
enhance understanding of early detection of oral cancer and pre-cancerous lesions.
Keywords: Normal, Non-malignant, Malignant lesions, Image classification, Roboflow
annotation software, YOLO v8 object/image classification algorithm. / Oral Biology
| Identifer | oai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/10255 |
| Date | 05 1900 |
| Creators | Solanki, Anusha, 0009-0006-9086-9165 |
| Contributors | Ogwo, Chukwuebuka CEO, Kuklani, Riya RK, DiPede, Louis LD |
| Publisher | Temple University. Libraries |
| Source Sets | Temple University |
| Language | English |
| Detected Language | English |
| Type | Thesis/Dissertation, Text |
| Format | 48 pages |
| Rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | http://dx.doi.org/10.34944/dspace/10217, Theses and Dissertations |
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