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The second-leading cause of death for women is breast cancer. Consequently, a precise early diagnosis is essential. With the rapid development of artificial intelligence, computer-aided diagnosis can efficiently assist radiologists in diagnosing breast problems. Mammography images, breast thermal images, and breast ultrasound images are the three ways to diagnose breast cancer. The paper will discuss some recent developments in machine learning and deep learning in three different breast cancer diagnosis methods. The three components of conventional machine learning methods are image preprocessing, segmentation, feature extraction, and image classification. Deep learning includes convolutional neural networks, transfer learning, and other methods. Additionally, the benefits and drawbacks of different methods are thoroughly contrasted. Finally, we also provide a summary of the challenges and potential futures for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century. AI and ML are revolutionizing human activities in various fields, and infectious diseases are not exempt from their rapid and exponential growth. Despite some notable challenges, explainable AI/ML could provide insights into the decision-making process, making the outcomes of models more transparent. Improved transparency can help to build trust among healthcare professionals, policymakers, and the general public in leveraging AI/ML-based systems to face the growing challenges of infectious diseases in the present century. [ABSTRACT FROM AUTHOR]

Cell counting and segmentation are critical tasks in biology and medicine. The traditional methods for cell counting are labor‐intensive, time‐consuming, and prone to human errors. Recently, deep learning‐based cell counting methods have become a trend, including point‐based counting methods, such as cell detection and cell density prediction, and non‐point‐based counting, such as cell number regression prediction. However, the point‐based counting method heavily relies on well‐annotated datasets, which are scarce and difficult to obtain. On the other hand, nonpoint‐based counting is less interpretable. The task of cell counting by dividing it into two subtasks is approached: cell number prediction and cell distribution prediction. To accomplish this, a deep learning network for spatial‐based super‐resolution reconstruction (SSRNet) is proposed that predicts the cell count and segments the cell distribution contour. To effectively train the model, an optimized multitask loss function (OM loss) is proposed that coordinates the training of multiple tasks. In SSRNet, a spatial‐based super‐resolution fast upsampling module (SSR‐upsampling) is proposed for feature map enhancement and one‐step upsampling, which can enlarge the deep feature map by 32 times without blurring and achieves fine‐grained detail and fast processing. SSRNet uses an optimized encoder network. Compared with the classic U‐Net, SSRNet's running memory read and write consumption is only 1/10 of that of U‐Net, and the total number of multiply and add calculations is 1/20 of that of U‐Net. Compared with the traditional sampling method, SSR‐upsampling can complete the upsampling of the entire decoder stage at one time, reducing the complexity of the network and achieving better performance. Experiments demonstrate that the method achieves state‐of‐the‐art performance in cell counting and segmentation tasks. The method achieves nonpoint‐based counting, eliminating the need for exact position annotation of each cell in the image during training. As a result, it has demonstrated excellent performance on cell counting and segmentation tasks. The code is public on GitHub (https://github.com/Roin626/SSRnet). [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]

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]

The complexity of AI-related theories may impede the applications of AI to MBD healthcare Deep learning (DL) is currently the most popular AI category. There are roughly six main categories of common AI methods [[21]]: deep learning, machine learning, neural networks, computer vision, robotics, and natural language processing, as shown in Figure 2. Big data have altered the way we manage, explore, evaluate, analyze, and leverage data across many different industries [[1]]. However, most MBD research thus far still focuses on either (i) traditional AI methods [[38]] due to the complexity of AI categories and sub-categories; or (ii) small-sized healthcare datasets involving the expensive collection of healthcare-related samples [[39]]. [Extracted from the article]

Cutting-edge developments in machine learning and deep learning are improving all aspects of cancer research and treatment. Nowadays, the applications of artificial intelligence, machine learning, and deep learning to clinical aspects of cancer research have received more attention from scholars, with particular emphasis on diagnosis, prognosis, detection, and treatment. [ABSTRACT FROM AUTHOR]

• 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]

• 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]

• 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]