Computer-aided diagnosis of Oral Squamous Cell Carcinoma: a feature-based transfer learning approach

Oral cancer, particularly Oral Squamous Cell Carcinoma (OSCC), has a high mortality rate due to late detection. However, manual diagnosis is difficult and time-consuming. Hence, the employment of machine learning methods has been explored to aid diagnosis through automated image classification. This study aims to evaluate pipelines combining pre-trained VGG19 convolutional neural network (CNN) model that is used to extract discriminative features from normal and cancerous oral histopathology images. The extracted features were fed to different machine learning models, support vector machine (SVM), k-nearest neighbours (kNN), and random forest (RF) were trained to classify the images. It was demonstrated that the VGG199-RF yielded the best performance across the training, validation, and test dataset with a classification accuracy of 99%, 92%, and 90%, respectively, against other pipelines evaluated. The study demonstrates that feature-based transfer learning is an attractive and effective approach to be employed for computer-aided diagnosis

The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning

Patients that are diagnosed with oral cancer has more than an 83% survival chance if it is detected in its early stages. However, through conventional labour-intensive means, only 29% of cases are detected. It is worth mentioning that 90% of oral cancer is Oral Squamous Cell Carcinoma (OSCC) and is often caused by smoking and alcohol consumption. Computer-aided diagnostics could further increase the rate of detection of this form of oral cancer. The present study sought to employ a class of deep learning techniques known as transfer learning. The Inception V3 pre-trained convolutional neural network model is used to extract the features from texture-based images. Consequently, the malignant and benign nature of the cancer is identified from three different machine learning models, i.e., Support Vector Machine (SVM), k-Nearest Neighbors (kNN) and Random Forest (RF). It was shown from the study that an average of 91% classification accuracy was obtained from the test and validation dataset from the Inception V3-RF pipeline. The outcome of the present study could serve useful in an objective-based automatic diagnostic of OSCC and hence could possibly increase its detection

The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning

Patients that are diagnosed with oral cancer has more than 83% sur- vival chance if it is detected in its early stages. However, through conventional labour-intensive means, only 29% cases are detected. It is worth to mention that 90 % of oral cancer is the Oral Squamous Cell Carcinoma (OSCC) and is often caused by smoking and alcohol consumption. Computer aided diagnostics could further increase the rate of detection of this form of oral cancer. The present study sought to employ a class of deep learning technique known as transfer learning. The Inception V3 pre-trained convolutional neural network model is used to ex- tract the features from texture-based images. Consequently, the malignant and benign nature of the cancel is identified from three different machine learning models, i.e., Support Vector Machine (SVM), k-Nearest Neighbors (kNN) and Random Forest (RF). It was shown from the study that an average of 91% classification accuracy was obtained from the test and validation dataset from the Inception V3-RF pipeline. The outcome of the present study could serve useful in an objective-based automatic diagnostic of OSCC and hence could possibly increase its detection