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BACKGROUND: Oral cancer is a malignant tumor that usually occurs within the tissues of the mouth. This type of cancer mainly includes tumors in the lining of the mouth, tongue, lips, buccal mucosa and gums. Oral cancer is on the rise globally, especially in some specific risk groups. The early stage of oral cancer is usually asymptomatic, while the late stage may present with ulcers, lumps, bleeding, etc. OBJECTIVE: The objective of this paper is to propose an effective and accurate method for the identification and classification of oral cancer. METHODS: We applied two deep learning methods, CNN and Transformers. First, we propose a new CANet classification model for oral cancer, which uses attention mechanisms combined with neglected location information to explore the complex combination of attention mechanisms and deep networks, and fully tap the potential of attention mechanisms. Secondly, we design a classification model based on Swim transform. The image is segmented into a series of two-dimensional image blocks, which are then processed by multiple layers of conversion blocks. RESULTS: The proposed classification model was trained and predicted on Kaggle Oral Cancer Images Dataset, and satisfactory results were obtained. The average accuracy, sensitivity, specificity and F1-Socre of Swin transformer architecture are 94.95%, 95.37%, 95.52% and 94.66%, respectively. The average accuracy, sensitivity, specificity and F1-Score of CANet model were 97.00%, 97.82%, 97.82% and 96.61%, respectively. CONCLUSIONS: We studied different deep learning algorithms for oral cancer classification, including convolutional neural networks, converters, etc. Our Attention module in CANet leverages the benefits of channel attention to model the relationships between channels while encoding precise location information that captures the long-term dependencies of the network. The model achieves a high classification effect with an accuracy of 97.00%, which can be used in the automatic recognition and classification of oral cancer. [ABSTRACT FROM AUTHOR]

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination. [ABSTRACT FROM AUTHOR]