Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer

Bansal, Khushboo; Bathla, R. K.; Kumar, Yogesh

doi:10.1007/s00500-022-07246-x

Cited by 21 publications

(11 citation statements)

References 41 publications

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“…sec2 Developments in the artificial intelligence has greatly helped the advancements in automating the cancer detection. Bansal et al [19] demonstrated the classification of malignant and non-malignant histopathological images using the deep transfer learning techniques such as ResNet50, MobileNetV2, VGG19, VGG16, and DenseNet along with the optimization technique. This has significantly increased the detection accuracy and reduces the error.…”

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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Section: Literature Surveymentioning

confidence: 99%

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

2024

Preprint

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“…Furthermore, Bansal et al 9 proposed deep hybrid transfer learning techniques for classifying and predicting oral cancer. They employed five pre-trained models and applied them to real-time and histopathological datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, Bansal et al 9 . proposed deep hybrid transfer learning techniques for classifying and predicting oral cancer.…”

Section: Related Workmentioning

confidence: 99%

Oral cancer detection using transfer learning-based framework from histopathology images

Redie,

Bilgaiyan,

Sagnika

2023

J. Electron. Imag.

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.Oral cancer is a serious worldwide health problem. It might be seen in the face, oral glands, neck, or mouth of the patient. Cancer detection utilizing histopathology images aids in easing and forecasting abnormality. Furthermore, it delivers better outcomes if biological procedures are implemented appropriately, however, there are large opportunities for human errors and blunders during physical examinations. The advancement of deep learning techniques may open the door to a better detection of oral cancer from histopathology images, which will be advantageous to lab staff and medical professionals. Our study presents an in-depth analysis of 10 pre-trained deep convolutional neural network models using transfer learning approach for the detection of oral cancer. The experiment was performed for two classes, i.e., normal and oral squamous cell carcinoma. The results show that the VGG19 model with data augmentation was able to attain the highest classification accuracy of 96.26% using the transfer learning technique. We have also introduced an approach by integrating the VGG19 pre-trained model with a custom naïve inception block. This fusion of two well-known models harnesses their complementary strengths and results in a more robust architecture. The incorporation of the inception block addresses limitations observed in the VGG19 framework, such as vanishing gradient issues and excessive computational requirements. The proposed model for detection of oral cancer has undergone a thorough block-wise fine tuning. Our results demonstrate the superior performance of our deep learning architecture compared to existing literature, highlighting its potential to enhance the detection and diagnosis of oral cancer.

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“…In [17], the authors analyzed oral mucosa disorders and their diagnosis through digital imaging and compared TNM (Tumor-Node-Metastasis) staging with a neural network for 75 patients, achieving high accuracy: 100% for T1, 85.19% for T2, 84.21% for T3, and 94.12% for T4. In [23], a study utilized deep transfer learning algorithms, like ResNet50, MobileNetV2, VGG19, VGG16, and DenseNet, on oral cancer images from histopathologic and real-time datasets. The most effective results were obtained with DenseNet using hybrid optimization techniques, achieving accuracies of 92.41% for real-time, 95.41% for oral cancer, and 92.41% for non-cancerous histopathologic images.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Islam,

Alam,

Uddin

et al. 2023

Diagnostics

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Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep learning-based image classification algorithms to identify oral lesions. We used three deep learning models, namely VGG19, DeIT, and MobileNet, to assess the efficacy of various categorization methods. To evaluate the accuracy and reliability of the models, we employed a dataset consisting of oral pictures encompassing two distinct categories: benign and malignant lesions. The experimental findings indicate that VGG19 and MobileNet attained an almost perfect accuracy rate of 100%, while DeIT achieved a slightly lower accuracy rate of 98.73%. The results of this study indicate that deep learning algorithms for picture classification demonstrate a high level of effectiveness in detecting oral lesions by achieving 100% for VGG19 and MobileNet and 98.73% for DeIT. Specifically, the VGG19 and MobileNet models exhibit notable suitability for this particular task.

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Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer

Cited by 21 publications

References 41 publications

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

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

Oral cancer detection using transfer learning-based framework from histopathology images

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

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