A histopathological image repository of normal epithelium of Oral Cavity and Oral Squamous Cell Carcinoma

Rahman, Tabassum Yesmin

doi:10.17632/ftmp4cvtmb.1

Cited by 6 publications

(6 citation statements)

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“…The dataset is divided into two parts with images at 100× and 400× magnification under a Leica DM 750 microscope (model ICC50 HD). The first set has 89 images of normal oral epithelium and 439 images of OSCC and the second set has 201 images of normal oral epithelium and 495 images of OSCC [21].…”

Section: Dataset Procurementmentioning

confidence: 99%

Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks

et al. 2023

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This work aims to classify normal and carcinogenic cells in the oral cavity using two different approaches with an eye towards achieving high accuracy. The first approach extracts local binary patterns and metrics derived from a histogram from the dataset and is fed to several machine-learning models. The second approach uses a combination of neural networks as a backbone feature extractor and a random forest for classification. The results show that information can be learnt effectively from limited training images using these approaches. Some approaches use deep learning algorithms to generate a bounding box that can locate the suspected lesion. Other approaches use handcrafted textural feature extraction techniques and feed the resultant feature vectors to a classification model. The proposed method will extract the features pertaining to the images using pre-trained convolution neural networks (CNN) and train a classification model using the resulting feature vectors. By using the extracted features from a pre-trained CNN model to train a random forest, the problem of requiring a large amount of data to train deep learning models is bypassed. The study selected a dataset consisting of 1224 images, which were divided into two sets with varying resolutions.The performance of the model is calculated based on accuracy, specificity, sensitivity, and the area under curve (AUC). The proposed work is able to produce a highest test accuracy of 96.94% and an AUC of 0.976 using 696 images of 400× magnification and a highest test accuracy of 99.65% and an AUC of 0.9983 using only 528 images of 100× magnification images.

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Section: Dataset Procurementmentioning

confidence: 99%

Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks

et al. 2023

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“…The first source is a histopathological image repository of oral squamous cell carcinoma. 15 There are 1224 images available in this dataset. The images were obtained using a Leica ICC50 HD microscope using tissue slides that had been gathered, processed, and classified by medical professionals from 230 individuals.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…16 The dataset in Ref. 15 was highly unbalanced in which the number of images in OSCC class were 934, whereas the number of images in normal class were just 290. To avoid this highly unbalanced problem and in order to substantially increase the number of input images, we have applied a data augmentation approach on the training dataset that will also enhance the model accuracy through minimizing the overfitting problem.…”

Section: Dataset Descriptionmentioning

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 order to validate the clinical significance of the computerized disease detection procedure, it is crucial to utilize a dataset consisting of histology slides collected from real patients. In this study, the OC dataset obtained from [10], which comprises 1224 H&E-stained histology slides captured using a Leica ICC50 HD microscope (Leica, Wetzlar, Germany), is employed for assessment. The dataset includes 518 images recorded at 100× magnification and 696 images captured at 400× magnification.…”

Section: Image Databasementioning

confidence: 99%

“…The slides were obtained from 230 patients using a Leica ICC50 HD microscope. The dataset contains two categories of images: 100× magnification (89 healthy and 439 OSCC) and 400× magnification (201 healthy and 495 OSCC) [10]. For this work, 1500 RGB-scaled images were extracted through image cropping, resulting in 1500 healthy and 1500 OSCC images for the proposed DL approach.…”

Section: Introductionmentioning

confidence: 99%

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection

et al. 2023

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Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework’s performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet’s validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.

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A histopathological image repository of normal epithelium of Oral Cavity and Oral Squamous Cell Carcinoma

Cited by 6 publications

References 0 publications

Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks

Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks

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

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection

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