# Enhancing clinical diagnosis of laryngeal cancer through fusion-based transfer learning with Osprey Optimisation Algorithm using histology images

## TL;DR

This paper proposes a new model for detecting laryngeal cancer using histology images and machine learning, achieving high accuracy.

## Contribution

A novel fusion-based transfer learning model with Osprey Optimisation Algorithm for improved laryngeal cancer detection.

## Key findings

- The CDLCHI-FTLOOA model achieved 97.16% accuracy in laryngeal cancer detection.
- Fusion of AlexNet, SqueezNet, and CapsNet improved feature extraction performance.
- Osprey Optimisation Algorithm enhanced model accuracy through optimal hyperparameter tuning.

## Abstract

Laryngeal squamous cell carcinoma is the most commonly diagnosed neck and head cancer. In contrast, the primary stage of pre-malignant and laryngeal cancer (LC) has to be handled with early diagnosis and treated with higher levels of laryngeal protection. Radiological evaluation with magnetic resonance imaging (MRI) and computed tomography (CT) techniques offers essential information on the disease in terms of the distance of the principal cancer and the existence of cervical lymph node metastasis. Recently, numerous deep learning (DL) and machine learning (ML) models have been implemented to classify the extracted features as either cancerous or healthy.

In this study, the Clinical Diagnosis of Laryngeal Cancer via Histology Images using the Fusion Transfer Learning and the Osprey Optimisation Algorithm (CDLCHI-FTLOOA) model is proposed. The aim is to improve the LC detection outcomes using histology image analysis to improve the patient’s life. Initially, the CDLCHI-FTLOOA model utilizes median filtering (MF)-based noise elimination during the image pre-processing process. Furthermore, the feature extraction process is performed by using the fusion models, namely AlexNet, SqueezNet, and CapsNet. The autoencoder (AE) method is employed for classification. To improve model performance, the Osprey Optimisation Algorithm (OOA) method is used for hyperparameter tuning to choose the optimal parameters for improved accuracy.

To exhibit the enhanced performance of the CDLCHI-FTLOOA model, a comprehensive experimental analysis is conducted under the laryngeal dataset. The comparison study of the CDLCHI-FTLOOA model portrayed a superior accuracy value of 97.16% over existing techniques.

Therefore, the proposed model can be employed for the accurate detection of the LC using the histopathological images.