Accurate and lightweight oral cancer detection using SE-MobileViT on clinically validated image dataset

<p>Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject’s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.</p> <p> </p>

Most Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases for an effective training. As these are not easily available, a good solution is to augment the databases with appropriate data augmentation (DA) techniques, which are typically based on either geometric transformation or oversampling augmentations (e.g., generative adversarial networks (GANs)). However, it is not always easy to understand which DA technique may be more convenient for FER systems because most state-of-the-art experiments use different settings which makes the impact of DA techniques not comparable. To advance in this respect, in this paper, we evaluate and compare the impact of using well-established DA techniques on the emotion recognition accuracy of a FER system based on the well-known VGG16 convolutional neural network (CNN). In particular, we consider both geometric transformations and GAN to increase the amount of training images. We performed cross-database evaluations: training with the "augmented" KDEF database and testing with two different databases (CK+ and ExpW). The best results were obtained combining horizontal reflection, translation and GAN, bringing an accuracy increase of approximately 30%. This outperforms alternative approaches, except for the one technique that could however rely on a quite bigger database.

Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middle-income countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

In this work, we design a neural network named MCRNN for classifying signals with slight distinction. This model combines advantages of both Convolutional Neural Network and Recurrent Neural Network, allowing the network to distinguish between long signals with long time-frequency maps. We use an architecture involving both convolutional layers, for extracting high-level features and reduce the dimension of data from time-frequency maps, and recurrent layers, for aggregating long-term dependencies. We apply techniques like Adam optimizer, batch normalization, and softmax layers, obtaining highly competitive results of 99.9% accuracy even in a low SNR of -2dB. Moreover, we conduct extensive experiment through a range of noisy scenarios from -10dB to 30dB. The model can be extended to signal dataset with short time-frequency maps easily. We leverage open source dataset like DeepSig as verification.

Smart home technology powered by cutting-edge intelligent algorithms creates a secure and comfortable living environment for electricity end users. In this paper, we propose an object surveillance algorithm and apply it to smart home environment. A combination usage of batch-normalized convolutional neural network (CNN) and data augmentation technique has been introduced for accomplishing object surveillance task by overseeing an object for long duration. Three aspects are considered including algorithm convergence, model creation, and dataset preparation. Firstly, mathematical demonstrations are offered to study the vanishing gradient phenomenon, and batch normalization is proposed for resolving this issue. Secondly, a revised version of AlexNet with batch normalization layer added after each convolution layer significantly boosts training speed and retains test set accuracy. Thirdly, only one picture taken on-premise is required for dataset preparation, and satisfactory predictive results are obtained.

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments.

Recent years, convolutional neural network (CNN) is a research hot spot in machine learning and has some application value in computer aided diagnosis. Firstly, this paper briefly introduces the basic principle of CNN. Secondly, it summarizes the improvement on network structure from two dimensions of model and structure optimization. In model structure, it summarizes eleven classical models about CNN in the past 60 years, and introduces its development process according to timeline. In structure optimization, the research progress is summarized from five aspects (input layer, convolution layer, down-sampling layer, full-connected layer and the whole network) of CNN. Thirdly, the learning algorithm is summarized from the optimization algorithm and fusion algorithm. In optimization algorithm, it combs the progress of the algorithm according to optimization purpose. In algorithm fusion, the improvement is summarized from five angles: input layer, convolution layer, down-sampling layer, full-connected layer and output layer. Finally, CNN is mapped into the medical image domain, and it is combined with computer aided diagnosis to explore its application in medical images. It is a good summary for CNN and has positive significance for the development of CNN.

In recent years, AI(Artificial Intelligence) has achieved great development in modern society. More and more modern technologies are applied in surveillance and monitoring. Healthcare monitoring is growing ubiquitous in modern wearable devices, such as a smart watch, electrocardiogram (ECG) necklace, smart band. Many sensors are attached to these smart devices to record and monitor physiological signals caused by activities, and then propagated those recorded electrical data to be further processed to give health diagnosis, disease prevention or making a distress call automatically. Obstructive sleep apnea (OSA) is a sleep disorder with a high occurrence in adult people and observed as an autonomous risk factor for circulatory problems such as ischemic heart attacks and stroke. Numerous traditional neural network based methods have been developed to detect OSA, where these methods however could not provide the intended result because they rely on shallow network. In this paper, we propose an effective OSA detection based on Convolutional neural network. Our method first extracts features from Apnea-Electrocardiogram (ECG) recordings using RR-intervals (time interval from one R-wave to the next R-wave in an ECG signal) and then CNN model having three convolution layers and three fully connected layers is trained with extracted features and applied for OSA detection. The first two convolution layers are followed by batch normalization and pooling layer, and softmax is connected to the last fully connected layer to give final decision. Experimental results on extracted feature of Apnea-ECG signal reveal that our model have better results in terms of performance measure sensitivity, specificity and accuracy. It is expected that the related technology can be applied into smart sensors, especially wearable devices.

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

Communication is not always direct; it often involves nuanced elements like humor, irony, and sarcasm. This study introduces a novel two-level approach for sarcasm detection, leveraging Convolutional Neural Networks (CNNs). Convolutional neural networks (CNNs) are crucial for many deep learning applications, yet their deployment on IoT devices is challenged by resource constraints and the need for low latency, particularly in on-device training. Traditional methods of deploying large CNN models on these devices often lead to suboptimal performance and increased energy consumption. To address this, our paper proposes an energy- efficient CNN design by optimising batch normalisation operations. Batch normalisation is vital for deep learning, aiding in faster convergence and stabilising gradient flow, but there has been limited research on creating energy-efficient and lightweight CNNs with optimised batch normalisation. This study proposes a 3R (reduce, reuse, recycle) optimisation technique for batch normalization. This technique introduces an energy-efficient CNN architecture. We investigate the use of batch normalization optimization to streamline memory usage and computational complexity, aiming to uphold or improve model performance on CPU- based systems. Additionally, we evaluate its effectiveness across diverse datasets, focusing on energy efficiency and adaptability in different settings. Furthermore, we analyze how batch normalization influences the performance and effectiveness of activation functions and pooling layers in neural network designs. Our results highlight batch normalization’s ability to enhance computational efficiency, particularly on devices with limited resources.

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.

In low- and middle-income countries, oral cancer is becoming more common. One factor delaying the discovery of oral cancer in rural areas is a lack of resources. To stop the disease from spreading, it is essential to quickly obtain information about any cancers. Therefore, it is essential to carry out early identification before it spreads. Primary screening is maintained in this study. Furthermore, deep neural network-based automated methods were used to produce complex patterns to address the challenging issue of assessing oral cancer infection. The goal of this work is to develop an Android application that uses a deep neural network to categorize oral photos into four groups: erythroplakia, leukoplakia, ulcer, and normal mouth. Convolutional neural networks and K-fold validation processes are used in this study’s methodology to create a customized Deep Oral Augmented Model (DOAM). Data augmentation techniques including shearing, scaling, rotation, and flipping are used to pre-process the images. A convolutional neural network is then used to extract features from the images Optimal configurations of max pooling layers, dropout, and activation functions have resulted in the attainment of maximum accuracies. By using the ”ELU” activation function in conjunction with RMSProp as the optimizer, the model achieves 96% validation accuracy, 96% precision, 96% F1 score, and 68% testing accuracy. The model is then deployed in TensorFlow Lite using an Android application.

Deep convolutional neural network (DCNN) is a powerful model for learning significant data at multiple levels of abstraction form an input image. However, training DCNN is often complicated because of parameter initialization, overfitting and convergence problems. Hence this work has been targeted to overcome the challenges of training DCNN with an optimized model. This chapter describes a deep learning framework for image classification with cifar-10 dataset. The model contains a set of convolutional layers with rectified linear unit activation function, max-pooling layers, and a fully-connected layer with softmax activation function. This model learns the features automatically and classifies the image without using the hand-crafted image based features. In this investigation, various optimizers have been applied in gradient descent technique for minimizing the loss function. Model with Adam optimizer constantly minimizes the objective function compared with other standard optimizers such as momentum, Rmsprop, and Adadelta. Dropout and batch normalization techniques are adapted to improve the model performance further by avoiding overfitting. Dropout function deactivates the insignificant node form the model after every epoch. The initialization of a large number of parameters in DCNN is regularized by batch normalization. Results obtained from the proposed model shows that batch normalization with dropout significantly improves the accuracy of the model with the tradeoff of computational complexity.

YOLOv8 powered deep learning framework for detection and classification of oral potentially malignant disorders and oral cancer using intraoral images.

Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach