Machine Learning Precision: Predicting Oral Cancer in Cell Carcinoma Biopsy Images through Deep Learning and Analytical Insights

Oral cancer is a pervasive and potentially fatal condition that impacts millions of individuals across the globe. Deep learning methods have demonstrated the potential to enhance the precision of oral cancer diagnosis by utilizing extensive datasets to detect patterns and provide predictions using novel data. This methodology entails the utilization of deep neural networks to examine photos of oral lesions, categorize them, and produce precise forecasts on the probability of malignancy. This study employs the ResNet 101 model, a deep-learning residual network, to assess the probability of acquiring the disease. The risk factors are examined through previous research, and the estimated magnitudes for each component are calculated from the data. Contemporary oncology uses deep learning algorithms to forecast and classify patient outcomes. This can lead to improved accuracy in diagnosing oral cancer, which has the potential to impact patient results and quality of life greatly.