Laryngeal Cancer Detection and Classification Using Aquila Optimization Algorithm With Deep Learning on Throat Region Images

Laryngeal cancer detection on throat area images is a vital application of medical image diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and detection of cancerous or abnormal tissues from the larynx, an essential part of the respiratory and vocal systems. Several machine learning (ML) and deep learning (DL) systems are executed for classifying the extraction features as both cancerous and healthy tissue. Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs) have shown promise in this context. With this motivation, this study designs a new Laryngeal Cancer Detection and Classification using the Aquila Optimization Algorithm with Deep Learning (LCDC-AOADL) technique on neck region images. The purpose of the LCDC-AOADL technique is to examine the histopathological images for the recognition and classification of Laryngeal Cancer. In the presented LCDC-AOADL technique, the Inceptionv3 model is used for the feature extraction process. Besides, the LCDC-AOADL technique employed a deep belief network (DBN) model for the identification and classification of LC. Moreover, the AOA is utilized for the hyperparameter tuning of the DBN model which results in improved detection rate. The simulation analysis of the LCDC-AOADL method is validated on the benchmark Laryngeal dataset. The experimental results pointed out the enhanced detection results of the LCDC-AOADL technique over other recent approaches with a maximum accuracy of 96.02%, precision of 92.10%, recall of 91.87%, and F-score of 91.86%.