Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images

Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images

Savita K. Shetty, Annapurna P. Patil
Copyright: © 2023 |Pages: 21
DOI: 10.4018/IJGHPC.320474
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Abstract

Earlier detection and classification of squamous cell carcinoma (OSCC) is a widespread issue for efficient treatment, enhancing survival rate, and reducing the death rate. Thus, it becomes necessary to design effective diagnosis models for assisting pathologists in the OSCC examination process. In recent times, deep learning (DL) models have exhibited considerable improvement in the design of effective computer-aided diagnosis models for OSCC using histopathological images. In this view, this paper develops a novel duck pack optimization with deep transfer learning enabled oral squamous cell carcinoma classification (DPODTL-OSC3) model using histopathological images. The goal of the DPODTL-OSC3 model is to improve the classifier outcomes of OSCC using histopathological images into normal and cancerous class labels. Finally, the variational autoencoder (VAE) model is utilized for the detection and classification of OSCC. The performance validation and comparative result analysis for the DPODTL-OSC3 model are tested using a histopathological imaging database.
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1. Introduction

Oral cancer, which is one of the most common and deadly diseases in the world, has been a significant issue for the general public's health for a good number of years. It is estimated that 475,000 new instances of the subtype of head and neck cancer known as oral cancer will be discovered each year. It is estimated that approximately 80% of patients will survive the disease's early stages, while only 20% will survive the disease's later stages (Amin, I., 2021 and Chu, C. S., 2021). Squamous cell carcinoma of the oral cavity is responsible for the majority of cases of oral cancer, which accounts for around 85 percent of all cases. Even though primary detection of oral cancer is vital, the majority of patients are diagnosed with the disease after it has progressed, leaving them with a poor prognosis. This is because early detection of oral cancer is essential. despite the fact that oral cancer screenings at an early stage are crucial. This is the situation, despite the fact that discovering oral cancer in its early stages is extremely important. The clinical appearance of oral cancer does not adequately represent the dysplastic condition, analysis, or severity of the illness; hence, therapeutic suggestions that are based solely on the clinical appearance are insufficient. Oral cancer has many risk factors and a low post-treatment survival rate (Arun, A. 2021 and Das, D. K., 2018). Biological and medical models of connected and lesion-free tumours can be detected in various body locations without stereotypes and appearance models. High-risk patients have leukoplakia, erythroplasia, and oral submucosal fibrosis. Malignant and benign tumours must be differentiated. Age, gender, and smoking affect oral cancer prognosis (Jain, M., 2014). AI can tackle every healthcare problem (Cyril, C. P. D., 2021). Unhealed sores or ulcers that are painful or bleeding often indicate malignancy. Symptoms of oral cancer include non-healing white or red lesions on the lips, gums, tongue, or mouth; a lump or tumour in the mouth; loose teeth; trouble speaking or swallowing; a swollen jaw; and chronic throat pain. Oral cancer can also spread to other parts of the body.

The most common type of cancer of the lips and oral cavity is oral squamous cell carcinoma (OSCC), which has a high mortality rate due to diagnostic delays brought on by early-stage misdiagnosis. The gold standards for diagnosing OSCC are histopathological analysis and biopsies, but these procedures are time-consuming, invasive, and not always acceptable to patients, especially when additional biopsies are required as part of a follow-up. The diagnostic approach is enhanced using adjunctive non-invasive imaging techniques, which also makes it more efficient and well-liked by patients. The goal of the current review is to highlight the most well-established diagnostic methods, such as tissue autofluorescence and vital staining, and to discuss the potential applications of some of the most innovative methods currently being developed, including narrow-band imaging, high-frequency ultrasounds, optical coherence tomography, and in vivo confocal microscopy. An ideal three-step diagnostic process is suggested considering their contribution to the diagnosis of OSCC to improve the speed, quality, and accuracy of the diagnostic process (Romano, A,2021).

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