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

Abstract

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%.

Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning

“…The upward trend in TS 𝑎𝑐𝑐𝑢 underlines the model's flexibility to the TR dataset and its capacity to create exact forecasts on unseen data, prominence robust generalization skills. 10 illustrate a comprehensive comparative analysis of ALCAD-DMODL methodology with other recent techniques [11]. The simulation values imply that the ALCAD-DMODL method has outperformed enhanced performances.…”

“…Alrowais et al [ 11 ] developed an innovative LCA Detection and Classification using the Aquila Optimizer Algorithm with DL (LCDC-AOADL) method. The Inceptionv3 architecture was employed for feature extraction.…”

“…Table 4 and Figure 10 illustrate a comprehensive comparative analysis of ALCAD-DMODL methodology with other recent techniques [ 11 ]. The simulation values imply that the ALCAD-DMODL method has outperformed enhanced performances.…”