DOAJ Open Access 2025

Oral Lesions Classification using EfficientNet Transfer Learning Model

Devika G Asha Gowda Karegowda

Abstrak

Due to their wide variety of diseases, oral lesions present a substantial diagnostic problem. This research uses deep learning techniques, particularly the EfficientNetB7 model, to present an automated categorisation analysis of oral lesions. The study divides lesions into benign and malignant categories using the Oral Lesions: Cancer Detection Dataset, comprising 2270 high-resolution pictures. Known for its effectiveness in processing large-scale image collections, the EfficientNetB7 architecture is employed in this work. The model successfully distinguishes between benign and malignant tumors with an exceptional accuracy rate of 99.12%. The study highlights the diagnostic dependability of the model by analyzing its performance, including metrics for sensitivity, specificity, and accuracy. Moreover, the study investigates how interpretable the model’s predictions are, emphasizing essential aspects that support its decision-making process.

Topik & Kata Kunci

Transportation engineering Systems engineering Environmental engineering Engineering economy Plasma engineering. Applied plasma dynamics Engineering machinery, tools, and implements Mechanics of engineering. Applied mechanics Materials of engineering and construction. Mechanics of materials Structural engineering (General) Engineering geology. Rock mechanics. Soil mechanics. Underground construction Architectural engineering. Structural engineering of buildings Computer engineering. Computer hardware Nuclear engineering. Atomic power Chemical engineering Low temperature engineering. Cryogenic engineering. Refrigeration Naval architecture. Shipbuilding. Marine engineering Business

Penulis (2)

Devika G

Asha Gowda Karegowda

Format Sitasi

APA MLA BibTeX

G, D., Karegowda, A.G. (2025). Oral Lesions Classification using EfficientNet Transfer Learning Model. https://doi.org/10.58482/ijeresm.v4i2.2

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.58482/ijeresm.v4i2.2
Akses: Open Access ✓