The Classification of Oral Squamous Cell Carcinoma (OSCC) by Means of Transfer Learning

Rauf, Ahmad Ridhauddin Abdul; Isa, Wan Hasbullah Mohd; Khairuddin, Ismail Mohd; Razman, Mohd Azraai Mohd; Arzmi, Mohd Hafiz; Majeed, Anwar P. P. Abdul

doi:10.1007/978-3-030-97672-9_34

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…The AUC value of the 3DCNN network model in the enhancement rate picture is 0.801, This is approximately 5% more than the Cost economy of the trial with a single upgraded picture. Ahmad Ridhauddin Abdul Rauf Et.al [11] present a study that Transfer learning was an attempt to use a class of deep learning techniques. To extract features from texture-based photos, the Inception V3 pre-trained convolutional neural network model is employed.…”

Section: Introductionmentioning

confidence: 99%

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Sagari¹,

Solanki²,

Goel³

et al. 2023

IJSREM

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Abstract—oral cavity cancer also called squamous cell carcinoma is the sixth most frequent cancer overall, it is a rising public health concern that is spreading across the globe. If not detected in earlier stages, it might be severe generally, and underlying lesions induce it. Therefore, it might be difficult to locate the lesion area and if not cured in its nascent stages, it might reach stage IV cancer, called the metastatic stage. To come up with a solution, we tend to use the three- dimensional CNN processes malignancies with image data and 3DCNN structures to classify benign and aggressive oral tumors. By identifying the kind of tumor region susing various kinds of knowledge here on periodic alterations in tumor enhancers, CNN was utilized in Test with visual categorization improvement rates. Analyzed, categorized, and future study areas are identified. The datasets utilized in the review articles are covered in detail, as are the pre- processing methods, the deep learning frameworks employed, etc. It presents a comparison of the same. Index Terms—Oral Cancer, Oral lesions, Oral Cavity, 3D CNN, Pre-processing, tumors, malignant, deep-learning

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Section: Introductionmentioning

confidence: 99%

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Sagari¹,

Solanki²,

Goel³

et al. 2023

IJSREM

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show abstract

“…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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“…Along with the need for oversight and result interpretation, automated diagnosis-related ethical issues should also be taken into mind. 4 Oral malignancy is a prevalent and life-threatening form of cancer. Recent advancements in ML have made it more convenient for doctors to utilize these techniques in medical image classification.…”

Section: Introductionmentioning

confidence: 99%

“…It is crucial to keep in mind that this study is only one examination. Along with the need for oversight and result interpretation, automated diagnosis‐related ethical issues should also be taken into mind 4 . Oral malignancy is a prevalent and life‐threatening form of cancer.…”

Section: Introductionmentioning

confidence: 99%

Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

Raza,

Alshehri,

Almakdi

et al. 2023

Int J Imaging Syst Tech

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Brain tumors pose a serious neurological threat to human life, necessitating improved detection and classification methods. Deep transfer learning (TL), in particular in key tumor categories such as meningioma, pituitary, glioma, and instances without tumors, has shown to be a new and successful method for tumor identification and classification. In this work, the efficacy of two pre‐trained TL methods—Inceptionv3 and DenseNet121—was examined for correctly classifying certain kinds of brain tumors. The experimental findings show that the DenseNet‐121 model, using the TL approach, performed better than other models in terms of accuracy for the identification and classification of brain tumors. The classification test results were impressive, with DenseNet‐121 reaching an astounding 99.95% accuracy and precision, recall, and F1‐measure scores of 97.7%, 92.1%, and 94.8%, respectively. DenseNet‐121 demonstrated 100% and 92.42% training and validation accuracies, respectively, highlighting its potential as an effective and precise diagnosis tool for brain malignancies.

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The Classification of Oral Squamous Cell Carcinoma (OSCC) by Means of Transfer Learning

Cited by 4 publications

References 13 publications

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

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

Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

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