Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques

Islam, Md. Monirul; Alam, K. M. Rafiqul; Uddin, Jia; Ashraf, Imran; Samad, Md Abdus

doi:10.3390/diagnostics13213360

Cited by 3 publications

(1 citation statement)

References 26 publications

(26 reference statements)

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“…Some of them have taken photographic images into consideration, but only for OSCC and other types. (2,14) Many researchers have concentrated on oral malignancies, either 'suspicious' or 'normal' (15) . But whether it is an ulcer or the beginning of oral cancer, it cannot be detected and can only be found after it has spread.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive Primary Screening of Oral Lesions into Binary and Multi Class using Convolutional Neural Network, Stratified K-fold Validation and Transfer Learning

Shah,

Pareek

2024

IJST

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Objectives: To develop a deep learning method using camera images that can effectively detect the preliminary phase of oral cancer, which has a high rate of morbidity and mortality and is a significant public health concern. If left untreated, it can result in severe deformities and negatively affect the patient's quality of life, both physically and mentally. Early detection is crucial owing to the rapid spread of the disease, where biopsy is the only option left. Therefore, it is essential to identify malignancies swiftly to prevent disease progression non-invasively. Methods: Three different scenarios are used in this study to analyze samples: CNN architecture, stratified K-fold validation, and transfer learning. For automated disease identification on binary datasets (normal vs. ulcer) and multiclass datasets (normal vs. ulcer vs. Leukoplakia), camera images are pre-processed with data augmentation. As a feature extractor in the model, transfer learning is used with pre-defined networks such as VGG19, InceptionNET, EfficientNET, and MobileNET weights. Findings: Using the proposed CNN architecture, the F1 score for image classification was 78% and 74% for photos showing hygienic mouths or ulcers, and 83%, 87%, and 84% for images showing normal mouths, ulcers, and leukoplakia. Using stratified 3-fold validation, the results were improved to 97%, and an EfficientNET achieves the highest results in a binary F1 score of 98% and a classification with multiple classes F1 scores of 98%, 87%, and 91%, respectively. Novelty: Previous studies have mostly concentrated on differentiating oral potentially malignant diseases (OPMD) from oral squamous cell carcinoma (OSCC) or on discriminating between cancerous and non-cancerous tissues. The objective is to diagnose patients with non-invasive procedures to classify ulcers, healthy mouths, or precancerous type "Leukoplakia" without requiring them to visit a doctor. Keywords: CNN, Transfer Learning, Oral Cancer, Ulcer, Leukoplakia, Stratified K-fold validation

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Section: Introductionmentioning

confidence: 99%

Non-invasive Primary Screening of Oral Lesions into Binary and Multi Class using Convolutional Neural Network, Stratified K-fold Validation and Transfer Learning

Shah,

Pareek

2024

IJST

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Improving brain tumor classification with combined convolutional neural networks and transfer learning

İncir,

Bozkurt

2024

Knowledge-Based Systems

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Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network

Tiryaki,

Torenek-Agirman,

Miloglu

et al. 2024

BMC Med Imaging

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Objective This study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs). Methods A dataset consisting of five classes, four tongue lesion classes (coated, geographical, fissured tongue, and median rhomboid glossitis), and one healthy/normal tongue class, was constructed using tongue images of 623 patients who were admitted to our clinic. Classification performance was evaluated on VGG19, ResNet50, ResNet101, and GoogLeNet networks using fusion based majority voting (FBMV) approach for the first time in the literature. Results In the binary classification problem (normal vs. tongue lesion), the highest classification accuracy performance of 93,53% was achieved utilizing ResNet101, and this rate was increased to 95,15% with the application of the FBMV approach. In the five-class classification problem of tongue lesion types, the VGG19 network yielded the best accuracy rate of 83.93%, and the fusion approach improved this rate to 88.76%. Conclusion The obtained test results showed that tongue lesions could be identified with a high accuracy by applying DCNNs. Further improvement of these results has the potential for the use of the proposed method in clinic applications.

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Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques

Cited by 3 publications

References 26 publications

Non-invasive Primary Screening of Oral Lesions into Binary and Multi Class using Convolutional Neural Network, Stratified K-fold Validation and Transfer Learning

Non-invasive Primary Screening of Oral Lesions into Binary and Multi Class using Convolutional Neural Network, Stratified K-fold Validation and Transfer Learning

Improving brain tumor classification with combined convolutional neural networks and transfer learning

Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network

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