Oral squamous cell detection using deep learning

TL;DR

This paper demonstrates the effectiveness of deep learning, specifically EfficientNetB3, in accurately detecting oral squamous cell carcinoma from medical images, potentially improving early diagnosis and patient outcomes.

Contribution

It introduces the application of EfficientNetB3 for OSCC detection, achieving high accuracy and precision, advancing deep learning methods in oral cancer diagnosis.

Findings

Achieved 98.33% accuracy in OSCC detection

High precision and recall of approximately 97.8%

Highlights potential for improved clinical diagnosis

Abstract

Oral squamous cell carcinoma (OSCC) represents a significant global health concern, with increasing incidence rates and challenges in early diagnosis and treatment planning. Early detection is crucial for improving patient outcomes and survival rates. Deep learning, a subset of machine learning, has shown remarkable progress in extracting and analyzing crucial information from medical imaging data.EfficientNetB3, an advanced convolutional neural network architecture, has emerged as a leading model for image classification tasks, including medical imaging. Its superior performance, characterized by high accuracy, precision, and recall, makes it particularly promising for OSCC detection and diagnosis. EfficientNetB3 achieved an accuracy of 0.9833, precision of 0.9782, and recall of 0.9782 in our analysis. By leveraging EfficientNetB3 and other deep learning technologies, clinicians can potentially improve the accuracy and efficiency of OSCC diagnosis, leading to more timely interventions and better patient outcomes. This article also discusses the role of deep learning in advancing precision medicine for OSCC and provides insights into prospects and challenges in leveraging this technology for enhanced cancer care.