A Novel Approach for Oral Cancer Classification and Survival Rate Prediction Using Advanced Deep Learning Approach

The rising rate of oral cancers (OC) which is (oral squamous cell carcinoma [OSCC]) more troubling still, the increasing rate of OSCC in younger populations adds significance to the need for valid and consistent prediction methods. More typical research methods for predicting OSCC and cancer in general are usually extremely computationally intensive and have extremely low prediction accuracy. An automated OC classification pipeline that employs deep recurrent spiking neural networks (DRSNN) has been developed. To develop this pipeline, the input was pre-processed by applying a median filter (MF), unsharp masking, and contrast-limited adaptive histogram equalization (CLAHE) to filter some noise and enhance the images. Then features were extracted using the Gabor Wavelet Transform (GWT). After feature extraction, a newly developed Hybrid Bowerbird-Grey Wolf Optimization (HBGWO) was implemented to determine the most representative features. Finally, the images were classified as normal or abnormal using the DRSNN classifier, hence outlining a quicker and efficient prediction methodology for OC detection. The framework was validated using the Kaggle Oral OSCC (lips and tongue images) dataset that was simultaneously augmented to 1,310 samples. According to the experimental data, the suggested method outperforms the F-measure, accuracy, specificity, and precision of the predictive models that have been in use for the past year and achieves an overall classification accuracy of 98.92% when compared favorably to current relevant predictive algorithms, such as ANN, DNN, DCNN-LSTM, and SVM.