Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture.

PROBLEM: Oral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment. AIM: This study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer. METHODS: A deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture. RESULTS: The improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field. CONCLUSION: Integrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

Using Verb Fluency, Natural Language Processing, and Machine Learning to Detect Alzheimer's Disease.

Alzheimer's disease (AD) causes significant impairments in memory and other cognitive domains. As there is no cure to the disease yet, early detection and delay of disease progression are critical for management of AD. Verbal fluency is one of the most common and sensitive neuropsychological methods used for detection and evaluation of the cognitive declines in AD, in which a subject is required to name as many items as possible in 30 or 60 seconds that belong to a certain category. In this study, we develop an approach to detect AD using a verb fluency (VF) task, a specific subset of verbal fluency analyzing the subjects' listing of verbs in a given time period. We use machine learning techniques including random forest (RF), neural network (NN), recurrent NN (RNN), and natural language processing (NLP) to detect the risk of AD. The results show that the developed models can stratify subjects into the corresponding AD and control groups with up to 76% accuracy using RF, but at a cost of having to preprocess the data. This accuracy is slightly lower, but not significantly, at 67% using RNN and NLP, which involves almost no manual preprocessing of the data. This study opens up a powerful approach of using simple VF tasks for early detection of AD.