DEEPDAPORCD: Oral Cancer Detection Using Deep Network & Distributed Affinity Propagation

DEEPDAPORCD: Oral Cancer Detection Using Deep Network & Distributed Affinity Propagation

© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : R. Dharani, S. Revathy
DOI :  10.14445/22315381/IJETT-V70I4P225

How to Cite?

R. Dharani, S. Revathy, "DEEPDAPORCD: Oral Cancer Detection Using Deep Network & Distributed Affinity Propagation," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 286-293, 2022. Crossref,

One of the most common cancers is oral cancer. Oral cancer appears to be on the rise all around the world. To separate cancerous lesions from contentious and malignant lesions present in the dental cavity, the doctor must go through a high level. Because there is no pain for the sufferer and they mimic several other lesions, cancer`s early stages and eventual manifestations are commonly misunderstood. The research describes a deep learning approach for classifying oral pictures as normal or abnormal. The Distributed Affinity Propagation (AP) algorithm partitioned the diseased patches. Using a deep learning system based on Appearance-based characteristics and Pattern-based features, the segmented cancer spots were further classified as "moderate" or "severe." The deep learning algorithm`s key benefit is that it only requires a small number of oral images for the proposed research`s categorization and diagnostic phases. Recall Rate, Classification Accuracy, Precision Rate, and Error Rate were used to compare the effectiveness of the presented approaches. The study`s findings revealed that a mix of deep learning methods effectively detected oral cancer.

Affinity Propagation, Appearance-based feature, Cancer Detection, Improved CNN, Medical Image Processing, Oral. Pattern-based features.

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