Enhancing Oral Squamous Cell Carcinoma Detection: A Transfer Learning Perspective on Histopathological Analysis Using ResNet-18, AlexNet, DenseNet-169, and DenseNet-201 with Cyclic Learning Rate
Keywords:
Oral squamous cell carcinoma, histopathologic analysis, transfer learning, deep learning, Convolutional Neural NetworkAbstract
: In this study, an innovative method is introduced for the early identification of Oral Squamous Cell Carcinoma (OSCC) by employing deep learning techniques to analyze histopathological samples. Four prominent neural network architectures, ResNet-18, AlexNet, DenseNet-169, and DenseNet-201, are utilized to scrutinize biopsy specimens for cancerous anomalies. The approach incorporates Cyclic Learning Rate (CLR) for dynamic adaptation of learning rates during the model's training. ResNet-18 benefits from skip connections to enhance gradient flow, while AlexNet and DenseNet architectures significantly contribute to precise image categorization. DenseNet's distinctive feature reuse mechanism effectively mitigates the vanishing gradient issue. The research underscores the potential of deep learning in enhancing early OSCC detection, offering a promising avenue for more efficient cancer screening and treatment.
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