Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques

Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increased. Artificial Intelligence has proved to be instrumental in development of various such techniques. The various types of AI like ML, NLP, RPA etc. are being used, which have streamlined and organised the Electronic Health Records (EHR) along with aiding the healthcare provider with decision making and sample and data analysis. This article also deals with the 3 major categories of diagnostic techniques - Imaging based, Pathology based and Preventive diagnostic techniques and what all changes and modifications were brought upon them, due to use of AI. Due to such a high demand, the investment in AI based healthcare techniques has increased substantially, with predicted market size of almost 188 billon USD by 2030. In India itself, AI in healthcare is expected to raise the GDP by 25 billion USD by 2028. But there are also several challenges associated with this like unavailability of quality data, black box issue etc. One of the major challenges is the ethical considerations and issues during use of medical records as it is a very sensitive document. Due to this, there is several trust issues associated with adoption of AI by many organizations. These challenges have also been discussed in this article. Need for further development in the AI based diagnostic techniques is also done in the article. Alongside, the production of such techniques and devices which are easy to use and simple to incorporate into the daily workflows have immense scope in the upcoming times. The increasing scope of Clinical Decision Support System, Telemedicine etc. make AI a promising field in the healthcare and diagnostics arena. Concluding the article, it can be said that despite the presence of various challenges to the implementation and usage, the future prospects for AI in healthcare is immense and work needs to be done in order to ensure the availability of resources for same so that high level of accuracy can be achieved and better health outcomes can be provided to patients. Ethical concerns need to be addressed for smooth implementation and to reduce the burden of the developers, which has been discussed in this narrative review article.

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.

A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

With the development of deep learning theory and the decrease of the cost of acquiring massive data, the image semantic segmentation algorithm based on Convolutional Neural Networks (CNNs) is gradually replacing the conventional segmentation algorithm by its high accuracy segmentation performance. By increasing the amount of training data and stacking more convolutional layers to form Deep Convolutional Neural Networks (DCNNs), a neural network model with higher segmentation accuracy can be obtained, but it faces the problems of serious memory consumption and long latency. For some special application scenarios, such as augmented reality and mobile interaction, real-time processing cannot be performed. To improve the speed of semantic segmentation while obtaining the most accurate segmentation results as possible, this paper proposes a semantic segmentation algorithm based on lightweight convolutional neural networks. Taking the computational complexity and segmentation accuracy into account, the algorithm starts from the perspective of extracting high-level semantic features and introduces a position-attention mechanism with richer contextual information to model the relationship between different pixels, avoiding the convolutional local perceptual field to be too small. To recover clearer target boundaries, a channel attention mechanism is introduced in the decoding part of the model to mine more useful feature channel information and effectively improve the fusion of low-level features with high-level features. By verifying the effectiveness of the above model on a publicly available dataset and comparing it with the more popular semantic segmentation methods, the model proposed in this paper has higher semantic segmentation accuracy and reflects certain advantages in objective evaluation.

Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images

The task of classification and localization with detecting abnormalities in medical images is considered very challenging. Computer-aided systems have been widely employed to address this issue, and the proliferation of deep learning network architectures is proof of the outstanding performance reported in the literature. However, localizing abnormalities in regions of images that can support the confidence of classification continues to attract research interest. The difficulty of using digital histopathology images for this task is another drawback, which needs high-level deep learning models to address the situation. Successful pathology localization automation will support automatic acquisition planning and post-imaging analysis. In this paper, we address issues related to the combination of classification with image localization and detection through a dual branch deep learning framework that uses two different configurations of convolutional neural networks (CNN) architectures. Whole-image based CNN (WCNN) and region-based CNN (RCNN) architectures are systematically combined to classify and localize abnormalities in samples. A multi-class classification and localization of abnormalities are achieved using the method with no annotation-dependent images. In addition, seamless confidence and explanation mechanism is provided so that outcomes from WCNN and RCNN are mapped together for further analysis. Using images from both BACH and BreakHis databases, an exhaustive set of experiments was carried out to validate the performance of the proposed method in achieving classification and localization simultaneously. Obtained results showed that the system achieved a classification accuracy of 97.08%, a localization accuracy of 94%, and an area under the curve (AUC) of 0.10 for classification. Further findings from this study revealed that a multi-neural network approach could provide a suitable method for addressing the combinatorial problem of classification and localization anomalies in digital medical images. Lastly, the study's outcome offers means for automating the annotation of histopathology images and the support for human pathologists in locating abnormalities.

Multiple-Object Detection and Segmentation Based on Deep Learning in High-Resolution Video Using Mask-RCNN

Imaging sensors with higher resolution and higher frame rates are becoming more popular for wide-area video surveillance (VS) and other applications as technology advances Using Mask-RCNN, we proposed Multiple-Object Detection and Segmentation in High-Resolution Video based on Deep Learning. The ResNet-50 ResNet-101 is used as the backbone in the proposed R-CNN Mask FPN model. The deep residual network’s design overcomes the problem of lower learning efficiency due to the network’s deepening. To reach the objective of the smallest overall error, the deep residual network divided the training series into one training block, minimizing the error of each block. It is roughly divided into five convolutional layer stages. The output scale is cut in half at each point. We used mixed precision FP16 and FP32 for training the model and achieved great speed in training time reduction in inference time for object. The COCO 2014 data set is used to train and validate the proposed model with mixed precision, leading to faster performance. The results of the experiments show that the proposed model can run at 30–48 frames per second with 85% accuracy.