Recent Advances in Medical Image Processing

Since medical imaging is a fundamental step in clinical diagnosis and treatment, medical image processing is an attractive field for researchers. Among the different applications of medical image processing, this paper focuses on the segmentation task using a customized deep convolutional neural network (CNN). The proposed network is developed based on the idea of improving a deep network performance and speeding up its learning process while using less parameters. Using the famous U-Net architecture which has proven its effectiveness in the segmentation field, the customization is done here by applying adaptive activation functions. In the proposed network, the U-Net complexity is reduced about 200 times by alleviating some parameters to accelerate the learning process. However, the important modification is the use of adaptive activation functions where each convolution layer learns its own data-adaptive activation function as a linear combination of 16 well-known basic functions. This modification successfully compensates the accuracy drop caused by parameter alleviation and also makes the model capable to be tuned with small amount of training data. Conducting several experiments on five famous retinal image datasets, namely DRIVE, STARE, CHASE, HRF, and ARIA, the proposed customized U-Net achieved 96%, 97%, 96%, 97%, and 95% accuracy in segmenting blood vessels, respectively. The proposed network also showed 98% accuracy on ISIC skin lesion dataset for segmenting the lesion area. The obtained results obviously confirm the high performance of the proposed customized network compared to the previous successful researches in handling the medical segmentation task. They also light the hope that many famous deep networks can benefit from these types of customization to become efficient compact models with the ability to handle lack of sufficient data.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms' application in medical image processing have facilitated their translation from the lab to practical clinical applications and education.

Nowadays, the number of patients with brain tumors is steadily increasing, diagnosis and isolation of the tumor play an important role in the process of treatment and surgery. Due to the high error of manual segmentation of the tumor, algorithms that perform this operation with less error are of great importance. Convolutional neural networks have made great progress in the field of medical imaging. The use of imaging techniques and pattern recognition in the diagnosis and automatic determination of brain tumors by MRI imaging reduces errors, human error and speeds up detection. The artificial convolutional neural network (CNN) has been widely used in the diagnosis of intelligent cancers and has significantly reduced the error rate. Therefore, in this paper, we present a new method using a combination of convolutional and multi-scale artificial neural network that has significantly increased the accuracy of tumor diagnosis. This study presents a multidisciplinary convolution neural network (MCNN) approach to classifying tumors that can be used as an important part of automated diagnosis systems for accurate cancer diagnosis. Based on the MCNN structure, which presents the MRI image to several deep convolutional neural networks of varying sizes and resolutions, the stage of extracting classical hand-made features is avoided. This approach proposes better classification rates than the classical methods. This study uses a multi-scale convolution technique to achieve a detection accuracy of 95/4%, which shows the efficiency of the proposed method.

Medical images are an important basis for medical diagnosis. Nowadays, Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. In the context of COVID-19, image recognition functions based on artificial intelligence have been gradually applied to the recognition of various medical images. In medical imaging, AI has shown great potential by revolutionizing diagnostic imaging, improving accuracy, efficiency, and improved patient outcomes. At present, the three types of AI application in the field of medical imaging mainly include image recognition and analysis, auxiliary diagnosis and treatment scheme optimization. Firstly, this paper introduces the current research progress of artificial intelligence technology. At the same time, the current deep learning algorithm based on one of the artificial intelligence algorithms has achieved good results in medical image processing, so as to carry out related application research. Secondly, according to the current situation of clinical diagnosis, aiming at the problems in the current society, this paper lists the existing research results. Finally, the future development trend of deep learning based on artificial intelligence technology is prospected, and the medical community is expected to seize the application opportunities of artificial intelligence in the medical field and meet the corresponding challenges.

Deep Learning has the potential to renovate healthcare; alternatively, extensive skill is vital to train such models. In medical imaging, a diagnosis with precision and speed is done through Artificial Intelligence (AI), which includes Deep Learning. When compared with healthcare professionals, the Deep Learning models achieved a similar level of diagnostic accuracy. As such, most of the diagnostic technique is systematic, as it has intelligent classification approaches with the computer-aided decision support systems. Deep Learning methods, such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), and Recurrent Neural Network (RNN), support the classification. In recent studies, CNN has been found to be a vital technique for extracting valuable features from a medical dataset of images and may result in higher accuracy. Artificial intelligence (AI) in the form of Deep Learning (DL) and within the field of medical imaging has created remarkable developments in the classification of medical imaging datasets.

Medical image segmentation is a crucial and intricate process in medical image processing and analysis. With the advancements in artificial intelligence, deep learning techniques have been widely used in recent years for medical image segmentation. One such technique is the U-Net framework based on the U-shaped convolutional neural networks (CNN) and its variants. However, these methods have limitations in simultaneously capturing both the global and the remote semantic information due to the restricted receptive domain caused by the convolution operation’s intrinsic features. Transformers are attention-based models with excellent global modeling capabilities, but their ability to acquire local information is limited. To address this, we propose a network that combines the strengths of both CNN and Transformer, called CoTrFuse. The proposed CoTrFuse network uses EfficientNet and Swin Transformer as dual encoders. The Swin Transformer and CNN Fusion module are combined to fuse the features of both branches before the skip connection structure. We evaluated the proposed network on two datasets: the ISIC-2017 challenge dataset and the COVID-QU-Ex dataset. Our experimental results demonstrate that the proposed CoTrFuse outperforms several state-of-the-art segmentation methods, indicating its superiority in medical image segmentation. The codes are available at https://github.com/BinYCn/CoTrFuse.

Art education has undergone significant transformation in the era of digitalization. With the advancement of Artificial Intelligence (AI) technology, there is tremendous potential to enhance the art learning experience. This research aims to elucidate how AI technology can be effectively utilized to improve art education in the context of digitalization. The study aims to introduce AI technology and explore its potential applications in enhancing the art learning experience. In the context of art learning experience, AI technology can provide broader access to digital art collections, online galleries, and virtual museums. Students can explore various artworks and cultures through digital platforms, thereby enriching their understanding and appreciation of art. The research findings reveal that AI technology can offer personalized guidance to students in developing their artistic skills. Intelligent AI systems can analyze students' artworks, provide tailored suggestions, and assist in enhancing their technical and creative abilities. In conclusion, the integration of AI technology in art education holds immense potential to elevate students' learning experiences. By expanding access, providing personalized guidance, and creating interactive experiences, AI technology enriches art learning in the era of digitalization. This is expected to provide greater insights and understanding of how AI technology can be harnessed to enhance the art learning experience in the context of digitalization. By optimizing the potential of AI, art education can become more inclusive, personalized, and engaging.

The application of artificial intelligence (AI) in medicine has garnered significant attention in recent years, offering new possibilities for improving patient care across various domains. For herpes zoster, a viral infection caused by the reactivation of the varicella-zoster virus, AI technologies have shown remarkable potential in enhancing disease diagnosis, treatment, and management. This study aims to investigate the current research status in the use of AI for herpes zoster, offering a comprehensive synthesis of existing advancements. A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three databases of Web of Science Core Collection, PubMed, and IEEE were searched to identify relevant studies on AI applications in herpes zoster research on November 17, 2023. Inclusion criteria were as follows: (1) research articles, (2) published in English, (3) involving actual AI applications, and (4) focusing on herpes zoster. Exclusion criteria comprised nonresearch articles, non-English papers, and studies only mentioning AI without application. Two independent clinicians screened the studies, with a third senior clinician resolving disagreements. In total, 26 articles were included. Data were extracted on AI task types; algorithms; data sources; data types; and clinical applications in diagnosis, treatment, and management. Trend analysis revealed an increasing annual interest in AI applications for herpes zoster. Hospital-derived data were the primary source (15/26, 57.7%), followed by public databases (6/26, 23.1%) and internet data (5/26, 19.2%). Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. Overall, this study provides a comprehensive overview of AI applications in herpes zoster from clinical, data, and algorithmic perspectives, offering valuable insights for future research in this rapidly evolving field. AI has significantly advanced herpes zoster research by enhancing diagnostic accuracy, predicting treatment outcomes, and optimizing disease management. However, several limitations exist, including potential omissions from excluding databases like Embase and Scopus, language bias due to the inclusion of only English publications, and the risk of subjective bias in study selection. Broader studies and continuous updates are needed to fully capture the scope of AI applications in herpes zoster in the future.

Bouyei Opera is one of the Nuo operas performed by the Bouyei people, the main ethnic group in Libo County, Guizhou Province. With a long history, Bouyei Opera has become an intangible cultural heritage (ICH) in China. However, the current development of Bouyei Opera is facing challenges in terms of talent cultivation, conservation of cultural heritage, as well as cultural promotion. Nevertheless, the advancement of artificial intelligence and information technology provide necessary technical support for the collection and promotion of China’s ICHs. In this paper, we first focused on collecting and classifying various Nuo operas in China. To address the enormous number of intangible cultural heritages and the disadvantages of relying on human resources to classify them, we utilized deep learning (DL) to annotate images and videos automatically. Specifically, we adopted clustering methods and a deep convolutional neural network (DCNN)- based unsupervised learning model to extract the semantic features for classifying all the ICHs. Then we obtained six types of databases covering the history, performance, style of singing, masks, cultural continuity, and troupes of Nuo operas, and designed web pages based on JavaScript frameworks according to these databases. We aim to provide a better visual experience to attract the public’s interest in Nuo operas. Finally, we used augmented reality (AR) technology to enhance the interaction at the terminal and offer a platform for the public to gain a broader understanding of non-heritage cultures and skills, thereby increasing the visibility of non-heritage products.

In the last few years, with the rapid development of artificial intelligence (AI) technology, its application in the medical field has become more and more in-depth and extensive, especially the research of computer vision technology in medical image analysis and recognition has achieved many results. It is recognized by experts engaged in imaging diagnosis in the medical field, which has provided a great boost to the development of precision medicine. However, the application and development of artificial intelligence in the field of medical imaging is still in its infancy, and faces many technical and ethical challenges, such as data quality issues, data security issues and clinical application issues. The application of AI technology in medical imaging needs to be further adjusted and improved in combination with social development and actual needs. In this paper, we discussed the usages of artificial intelligence in medical imaging and provided new sights for the sustainable development and improvement in this area, as well as the challenges faced by others.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

MIDC: Medical image dataset cleaning framework based on deep learning

Intelligent diagnostic systems significantly enhance the effectiveness and efficiency of cancer detection and management, ultimately leading to better patient outcomes. According to statistics, cancer is the second prime cause of death in males. It's a sluggish-growing ailment that doesn't show symptoms until it's quite evolved. Various investigations on AI (Artificial Intelligence) algorithms analysis have been done in the previous few years over varied medical imaging modalities which includes Computed Tomography, Magnetic Resonance Imaging, and Ultrasound. The use of artificial intelligence to monitor prostate cancer would have a tremendous impact on healthcare. Cancer scientists would have a superior understanding of the ailment and it would be helpful in developing a more precise mechanism for cancer detection as it is the need of the hour, as it has been predicted that there will be over 1.3 million additional cases diagnosed annually around the world. Here an attempt has been made to provide an analysis of the progress being made in the sector of medical image processing. Also, based on the rising interest in CNN (Convolutional Neural Networks) in recent years, we have examined the use of CNN in numerous automatic processing tasks for prostate cancer identification and diagnosis. In this study, a novel deep learning convolutional neural network (CNN) model was employed and its performance was compared against three established CNN models: AlexNet, GoogleNet, and ResNet. It has been found that the use of CNN has increased dramatically, with excellent outputs gained using either new models or pre-conditioned networks for transfer learning. Deep learning-based research surpasses traditional patient prognostic methods with regard to accuracy, according to the survey's findings.

The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks