Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances vision and artificial intelligence (AI) techniques have resulted the design of CAD models, which enables to detection existence diseases using various imaging modalities. Oral cancer (OC) has commonly occurred head neck globally. Earlier identification OC improve survival rate reduce mortality rate. Therefore, classification essential. this study introduces a novel Computer Aided Diagnosis Sailfish Optimization with Fusion based Classification (CADOC-SFOFC) model. proposed CADOC-SFOFC determines on medical images. To accomplish this, fusion feature extraction process is carried out by use VGGNet-16 Residual Network (ResNet) Besides, vectors are fused passed into extreme learning machine (ELM) process. Moreover, SFO algorithm utilized parameter selection ELM model, consequently resulting enhanced performance. experimental analysis was tested Kaggle dataset results reported betterment over compared methods maximum accuracy 98.11%. potential as inexpensive non-invasive supports screening enhances efficiency.