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Fighting against COVID-19: Innovations and applications Abbreviations AI artificial intelligence CNN convolutional neural network CT computerized tomography CXR chest x-ray radiograph DL deep learning DNN deep neural network DSS decision support system ML machine learning COVID-19 has triggered over 6.90 million deaths worldwide until the cutoff date of April 23, 2023. Furthermore, chest CT can be used to evaluate the response to treatment in COVID-19 patients.[33] Repeat chest CT scans can show whether the lung damage is improving or stabilizing, which can help healthcare professionals to adjust the treatment plan as needed.[34] In this virtual special issue, many CT-based AI models are reported. COLI-Net: deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. [Extracted from the article]

• We analysed over 350 references from all well-famed databases. • We provided a comprehensive survey on deep learning in food category recognition. • This review encompassed current challenges & applications, strengths & limitations. • Fundamental deep learning rules and performance assessment methods were reviewed. • Convolutional neural networks, transfer and semi-supervised learning were reviewed. Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach's potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applications. [ABSTRACT FROM AUTHOR]

It relies on the synergy between artificial intelligence (AI) and software engineering, improving both software productivity and quality, for example, providing coding suggestions for better productivity or locating defects to avoid poor quality. Intelligent software engineering is an emerging topic that has attracted great attention in both academia and industry. The first paper, titled 'Syntax-based metamorphic relation prediction via the bagging framework' by Li et al., proposes a multi-dimensional program structure-based metamorphic relation prediction approach, which is composed of feature extraction and prediction model building. [Extracted from the article]

Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Simple Summary: Deep learning has succeeded greatly in medical image-based cancer diagnosis. To help readers better understand the current research status and ideas, this article provides a detailed overview of the working mechanisms and use cases of commonly used radiological imaging and histopathology, the basic architecture of deep learning, classical pretrained models, common methods to overcome overfitting, and the application of deep learning in medical image-based cancer diagnosis. Finally, the data, label, model, and radiomics were discussed specifically and the current challenges and future research hotspots were discussed and analyzed. (1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images. [ABSTRACT FROM AUTHOR]

• This paper summarizes 181 studies on artificial intelligence used to help the visually impaired. • DL can help diagnose eye diseases, including cataracts, diabetic retinopathy, and glaucoma. • DL can help build assisted reading and obstacle avoidance systems. The eyes are an essential tool for human observation and perception of the world, helping people to perform their tasks. Visual impairment causes many inconveniences in the lives of visually impaired people. Therefore, it is necessary to focus on the needs of the visually impaired community. Researchers work from different angles to help visually impaired people live normal lives. The advent of the digital age has profoundly changed the lives of the visually impaired community, making life more convenient. Deep learning, as a promising technology, is also expected to improve the lives of visually impaired people. It is increasingly being used in the diagnosis of eye diseases and the development of visual aids. The earlier accurate diagnosis of the eye disease by the doctor, the sooner the patient can receive the appropriate treatment and the better chances of a cure. This paper summarises recent research on the development of artificial intelligence-based eye disease diagnosis and visual aids. The research is divided according to the purpose of the study into deep learning methods applied in diagnosing eye diseases and smart devices to help visually impaired people in their daily lives. Finally, a summary is given of the directions in which artificial intelligence may be able to assist the visually impaired in the future. In addition, this overview provides some knowledge about deep learning for beginners. We hope this paper will inspire future work on the subjects.. [ABSTRACT FROM AUTHOR]

The approaches based on vision transformers (ViTs) are advancing the field of medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have developed artificial intelligence methods for cancer diagnosis based on ViTs. In this paper, 98 pertinent articles since 2020 were carefully chosen from digital databases, including Google scholar, Elsevier, and Springer Link, to review the research progress of artificial intelligence methods for cancer imaging based on ViT. Method : The basic structure of ViT is introduced, and corresponding modules such as patch embedding, positional embedding, transformer encoder, multi-head self-attention (MSA), layer normalization (LN), and residual connections, multilayer perceptron (MLP) are elaborated; a comprehensive review of improved ViT models in the medical field is presented. The application of ViT technology in cancer analysis based on medical images was reviewed. Results : ViT has achieved great success in cancer diagnosis based on medical images, showing its advantages in image classification, image reconstruction, image detection, image segmentation, image registration, image fusion, and other tasks. In these task studies, the most common task is cancer image classification and segmentation. There is still a lot of room for improvement in the aspects of multi-task learning, multi-modal learning, model generality, generalization ability, and explainability, and it also faces the mutual restriction of model scale and performance. Conclusion : The ViT training model for cancer diagnosis can potentially improve. The ViT model of self-supervised learning and semi-supervised learning mechanism is promising research. The lightweight attention module design, ViTs based on mobile networks, and the development of 3DViT will promote cancer diagnosis based on medical images to be more accurate and efficient. [ABSTRACT FROM AUTHOR]

• More than 245 references from top journals are analyzed. • A systematic theoretical analysis of deep learning in dentistry is provided. • Extensive works on deep learning systems of dentistry are well summarized. • Future challenges of deep learning methods in dentistry are analyzed. Oral diseases have a significant impact on human health, often going unnoticed in their early stages. Deep learning, a promising field in artificial intelligence, has shown remarkable success in various domains, especially dentistry. This paper aims to provide an overview of recent research on deep learning applications in dentistry, with a focus on dental imaging. Deep learning algorithms perform well in difficult tasks such as image segmentation and recognition, enabling accurate identification of oral conditions and abnormalities. Integration of deep learning with other oral health data offers a holistic understanding of the relationship between oral and systemic health. However, there are still many challenges that need to be addressed. [ABSTRACT FROM AUTHOR]

The article present the authors' comment on the study "Intelligent medical disease diagnosis using improved hybrid genetic algorithm-multilayer perception network," by F. Ahmad and colleagues, published in a 2013 issue of the "Journal of Medical Systems." Topics discussed include the utilization of machine learning and computer vision in the development of the diagnosis system, corrected form to equation two, and possible applications of the results.

One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS), which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases) from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS) as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient. [ABSTRACT FROM AUTHOR]