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Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017–2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas. [ABSTRACT FROM AUTHOR]

Rapid advancement in additive manufacturing capabilities and computational resources has catalyzed multiscale topology optimization (TO). Recently, TO in general and multiscale TO in particular have received increased attraction due to its systematic approach to design globally and locally optimized structures. The multiscale TO computational paradigm brings additional new challenges, including geometric frustration, non-smooth boundaries, and higher computational time that needs to be handled. In this paper, a novel deep learning-based computational pipeline is developed to overcome the challenges encountered in the multiscale TO framework. The outlined computational pipeline employs two neural networks (NNs) architecture: (1) SILONet and (2) ConnectivetyNet. SILONet predicts optimized microstructure for elements of macroscale optimized structure, and ConnectivetyNet improves connectivity for multiscale topology fitted with SILONet outputs. The main novelty of this paper is the utilization of two NNs architecture for alleviating challenges, such as geometric frustration, non-smooth edges, dangling structures at boundaries, and accelerating multiscale TO computations while capturing essential physics and ensuring optimized solutions. The effectiveness of the proposed approach is demonstrated by its application to several two-dimensional and three-dimensional test cases. [ABSTRACT FROM AUTHOR]

The explosive growth of geospatial data is increasing requirements for automatic and efficient data learning abilities. Many deep learning methods have been widely applied for geospatial data understanding tasks, such as road networks and geospatial object detection. However, the demands for more accurate learning of high-level features require the use of deeper neural networks. To further improve the learning efficiency of deep neural networks, in this paper, we propose an improved convolutional neural network named EDense. First, we use its dense connectivity to integrate a CNN with an extreme learning machine. Then, we expand the kernels in the convolutional layers to increase the width of the network model. Furthermore, we propose one-feature EDense (OF-EDense), which is a simplified version of EDense, to fit conditions in which the number of parameters is strictly limited. Finally, the experimental results fully demonstrate the strong learning ability and high learning efficiency of EDense. [ABSTRACT FROM AUTHOR]

This paper introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. The main novelty of this framework is the utilization of parameters such as density and nodal deflections to predict optimized topologies for a wide range of design space. In this research, a three-stage NN framework is employed, wherein the first stage is a feedforward deep neural network. The second stage uses a convolutional neural network (CNN), and the final stage is the post-processing of results obtained from CNN. The first stage maps input design variables vector to output density distribution image. The image output of the first stage and design variables vector inputted to the first network serve as inputs to the second stage, which outputs optimized structure. Finally, post-processing of this optimized structure is done to ensure volume fraction constraint and optimal solution. These structures can be tessellated per the nodal displacement of an FEA solved larger structure. Because of this framework, the proposed approach makes TO computations faster and captures essential physics while using NNs to determine the final structure. The application of the proposed model to three-dimensional problems is also demonstrated. [ABSTRACT FROM AUTHOR]

Crop disease serves as a major threat to the farming sector. Due to the increased utilization of smartphones, it is now possible to leverage the technology and apply it for the betterment of the farming sector. The agricultural sector struggles in supporting the ever-growing global population, moreover, plant disease reduces the amount of food production and quality of the food. Losses may be a cataclysm, but on an average, it affects almost 45% of the production of major crops. Farmers often spend a lots of money on disease management of the crops and each crop is vulnerable to a particular disease that affects the quality and final yield. But, lack of proper technology, results in poor disease management, soil pollution, and the outcome may be devastating. In addition, plant diseases also affect the food chain supply, destroy the natural ecosystem and contribute to exacerbating environmental issues. These problems can be eradicated by adopting deep learning algorithms to analyze and visualize the current condition of the crops. With application built using deep learning, it is now possible to accurately detect crop diseases thereby reducing the effects of crop disease on food supply. Thus, correct disease detection followed by the management of identified diseases, thereby increasing food production and maintaining the quality of the food is achieved by deep neural networks. The proposed model uses MobileNet architecture along with complex hidden layers fine-tuned with Keras tuner on the dataset containing 12,318 images. We proposed an enhanced MobileNet scalable model with better generalization on large sized unified dataset constructed from various smaller sized dataset for better features' extraction and representation. The proposed model classifies the input in 64 different classes for 22 different sets of crops and achieved an accuracy of 95.94%. Further, the model is inculcated with our Android application – Plantscape for a better user experience fusioned with serene user interactions. [ABSTRACT FROM AUTHOR]