Histopathological Image Analysis for Oral Squamous Cell Carcinoma classification using concatenated deep learning models

Amin, Ibrar; Zamir, Harris; Khan, Faisal F

doi:10.1101/2021.05.06.21256741

Cited by 26 publications

(24 citation statements)

References 22 publications

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“…In order to highlight the enhanced performance of ISMA-AIOCC technique, a comparison study was conducted and the results are shown in Fig. 12 [21]. The experimental results indicate that Visual Geometry Group (VGG16) and Support Vector Machine (SVM) models obtained the least classification performance.…”

Section: Resultsmentioning

confidence: 99%

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Shankar¹,

Lydia²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert's knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification (ISMA-AIOCC) model on Histopathological images (HIs). The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs. At the initial stage, linear smoothing filter is applied to eradicate the noise from images. Besides, MobileNet model is employed to generate a useful set of feature vectors. Then, Bidirectional Gated Recurrent Unit (BGRU) model is exploited for classification process. At the end, ISMA algorithm is utilized to fine tune the parameters involved in BGRU model. Moreover, ISMA algorithm is created by integrating traditional SMA and Chaotic Oppositional Based Learning (COBL). The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.

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Section: Resultsmentioning

confidence: 99%

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Shankar¹,

Lydia²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Ibrar et al [ 6 ] presented conversion learning based on adapting deep learning to diagnose histopathological images to diagnose OSCC. They extracted and categorized deep features using three CNN models; the models achieved an accuracy of 89.16% with VGG16.…”

Section: Related Workmentioning

confidence: 99%

“… Comparison of the performance of our system with previous studies for diagnosing oral squamous cell carcinomas [ 6 , 10 , 16 , 37 , 38 , 39 ]. …”

Section: Figurementioning

confidence: 99%

Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches

2022

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Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.

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“…And the number of operations that corresponds to it is equation (6). The total number of weights in depthwise separable convolution is equation ( 7), as well as the total number of operations in equation (8).…”

Section: Depthwise Separablementioning

confidence: 99%

Aiding Oral Squamous Cell Carcinoma diagnosis using Deep learning ConvMixer network

Nguyen

2022

Preprint

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In recent years, Oral squamous cell carcinoma (OSCC) has become one of the world's most prevalent cancers, and it is becoming more prevalent in many populations. The high incidence rate, late diagnosis, and inadequate treatment planning continue to be major concerns. Despite the enhancement in the applications of deep learning algorithms for the medical field, late diagnosis, and approaches toward precision medicine for OSCC patients remain a challenge. Due to a lack of datasets and trained models with low computational costs, the diagnosis, an important cornerstone, is still done manually by oncologists. Although Convolutional neural networks (CNNs) have become the dominant architecture for vision tasks for plenty of years, recent experiments show that Transformer-based models, most noticeably the Vision Transformer (ViT), may out-compete them in some settings. Therefore, in this research, a method called ConvMixer, which combines great features from CNNs and patches based on ViT was applied to an original very small dataset of only 1224 images in total for 2 classes, Normal epithelium of the oral cavity (Normal) and OSCC, 696 slides for 400x magnification and 528 slides for 100x magnification. However, the proposed models with small parameters and data augmentation performed magnificently, with 400x magnification (Accuracy: 99.81% - F1score: 99.87%) and 100x magnification (Accuracy: 99.62% - F1score: 99.77%).

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Histopathological Image Analysis for Oral Squamous Cell Carcinoma classification using concatenated deep learning models

Cited by 26 publications

References 22 publications

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches

Aiding Oral Squamous Cell Carcinoma diagnosis using Deep learning ConvMixer network

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