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

Shetty, Savita; Patil, Annapurna P.

doi:10.4018/ijghpc.320474

Cited by 5 publications

(4 citation statements)

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“… Precision Recall F1-score Sensitivity Specificity AUC 100.0% 99.0% 99.0% 0.99 1.0 0.99 19 Kavyashree C. et al, 2022 [ 37 ] India Histopathological images (OSCC) 526 images (70:15:15 for training, validation, and testing) N/A N/A - CNN - DenseNet201 - DenseNet121 - DenseNet169 50 epochs Learning rate = 0.0001 Loss function: Binary Crossentropy N/A Precision Recall F1-score Accuracy TPR FPR 98.9% 98.9% 93.2% 85.0% 0.93 0.14 20 Arujuaid A. et al, 2022 [ 38 ] USA Histopathological images (OSCC) 448 images Annotated by oral pathologists N/A - GoogLeNet - InceptionV3 - Transfer learning N/A N/A Precision Recall F1-score Accuracy 90.0% 95.5% 92.8% 92.5% 21 Krishna S. et al, 2022 [ 39 ] India Histopathological images (OSCC) 1224 images N/A N/A - CNN - VGG16 - ResNet50 - Ensemble - Learning (VGG16+ ResNet50) N/A N/A Accuracy 62.50% 22 Sharma D. et al, 2022 [ 40 ] India Clinical oral images (OSCC) 329 images (70:10:20 for training, validation, and test) N/A flipping, zooming, and rotation - VGG19 - VGG16 - MobileNet - InceptionV3 - ResNet50 50 epochs Batch size = 16 Learning rate = 0.001 Tesla 1xK80 graphics card Precision Recall F1-score Accuracy 60.0% 43.0% 50.0% 76.0% 23 Shetty SK. et al, 2022 [ 41 ] ...…”

Section: Resultsmentioning

confidence: 99%

Deep learning in oral cancer- a systematic review

Warin,

Suebnukarn

2024

BMC Oral Health

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Background Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer. Methods This systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023. Results Fifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0–100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78–0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77–4687.39) for classification studies. Conclusions The number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer.

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Section: Resultsmentioning

confidence: 99%

Deep learning in oral cancer- a systematic review

Warin,

Suebnukarn

2024

BMC Oral Health

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“…The Inceptionv3 model provides the better performance with the finest classification accuracy. Shetty et al [23] proposes a unique duck optimization technique combined with transfer learning for oral cancer detection. The OSCC is detected and classified using the Variational Encoder (VAE) model.…”

Section: Literature Surveymentioning

confidence: 99%

An improved deep convolutional neural network fororal cancer detection using pretrained models

2024

Preprint

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Purpose: Oral cancer is a type of cancer that arises from Oral Squamous Cell Carcinoma (OSCC) that develops in the mouth. Oral cancer is a major global public health issue, which emphasizes the urgent need in the research domain for targeted and effective approaches that enable timely identification. The current diagnostic approaches has a limitation of delay and inefficiency. Artificial Intelligence (AI) is nowadays extensively used the cancer diagnosis and prognosis can provide a fast results that helps in early detection of cancer and improves the survival rate of the cancer patients. Deep learning techniques offers several pretrained models in automating the cancer detection. The research focus on developing a light weight architecture with improved results and low computational costs based on DenseNet architecture of the Convolutional Neural Network (CNN). Methods: The number of dense blocks are reduced without compromising the classification of histopathology images as benign and malignant. The histopathology images are preprocessed in a way that is suitable to fed to a neural network. The hyper parameters of the model is fine tuned to obtained the better results. Results: The proposed model is evaluated with the standard performance metrics and compared with the other pretrained models. The proposed model provides 98.96% of classification accuracy in training and 82.49% in validation. The loss also has been reduced with a good precision of 0.98, recall of 0.76 and F1 score of 0.82. Conclusion: The proposed model also takes care of overfitting and vanishing gradient problem that could hamper the models performance. This will also help a medical practitioner can utilize these findings to formulate initial decisions and plan treatment for oral cancer.

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“…With the most recent advancements in machine learning, numerous deep learning-based techniques, including convolutional neural network (CNN), pre-trained deep CNN networks [17], like Alexnet, VGG 16, VGG 19, ResNet 50 [18], MobileNet [19], multimodal fusion with CoaT (coat-lite-small), PiT (pooling based vision transformer pits-distilled-224), ViT (vision transformer small-patch16-384), ResNetV2 and ResNetY [20], and concatenated models of VGG 16, Inception V3 [21], have been proposed for the automated extraction of morphological features. After the feature extraction, the images were classified into normal and OSCC categories using different classifiers such as random forest [22], support vector machine (SVM) [10], extreme gradient boosting (XGBoost) with binary particle swarm optimization (BPSO) feature selection [23], K nearest neighbor (KNN) [10], duck patch optimization based deep learning method [24] and two pretrained models, ResNet 50 and DenseNet 201 [11]. However, as the number of layers of the network increases, the complexity also will increase.…”

Section: Introductionmentioning

confidence: 99%

“…[11] [18][20] [21][22] [23][24] using the public OSCC dataset, in terms of accuracy, precision and sensitivity. The results are summarised in Table…”

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confidence: 99%

An image patch selection algorithm for the detection of Oral Squamous Cell Carcinoma using textural and morphological features

Subhija

Reju

2023

Preprint

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A significant proportion of cancer-related deaths are caused by oral cancer, and oral squamous cell carcinoma is prevalent. Systems for computer-aided diagnosis can lower subjective errors and assist the pathologist in making a more accurate diagnosis. Feature extraction from the whole histopathology images is difficult due to the structural variation of the oral tissue images. A new patch selection algorithm can be used to create and select image patches containing nuclei-specific information and extract their textural and morphological characteristics, improving cancer diagnosis. We extract the morphological characteristics of the nucleus from the selected patches using five pretrained networks. The texture from the regions of the selected patches is also extracted from the Haar wavelet decomposed components using the gray-level co-occurrence matrix. Then, we combine the textural and morphological features to create the final feature vector, followed by feature selection using an extra trees classifier. In order to detect oral squamous cell carcinoma, we examined the feasibility of using six classifiers, including voting classifier, logistic regression, random forest, Naive Bayes, K nearest neighbor, and support vector machine. The performance of the algorithm is evaluated using accuracy, precision, sensitivity, confusion matrix, ROC curves, and AUC values of characteristic curves. Together, ResNet 50 and the voting classifier produce results with a high accuracy of 97.66 % and a precision of 98.00 %. The suggested patch-based method outperforms the image based method and is accurate and efficient for identifying oral squamous cell carcinoma and will be a reliable and precise support tool for oral pathologists.

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

Cited by 5 publications

References 32 publications

Deep learning in oral cancer- a systematic review

Deep learning in oral cancer- a systematic review

An improved deep convolutional neural network fororal cancer detection using pretrained models

An image patch selection algorithm for the detection of Oral Squamous Cell Carcinoma using textural and morphological features

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