Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM....
Wavelets are actively used to solve a wide range of image processing problems in various fields of science and technology. Modern image processing systems cannot keep up with the rapid growth in digital visual information. Various approaches are used to reduce the computational complexity and increase computational speeds. The Winograd method (WM) is one of the most promising. However, this method is used to obtain sequential values. Its use for wavelet image processing requires expanding the calculation methodology to cases of downsampling. This paper proposes a new approach to reduce the computational complexity of wavelet image processing based on theWMwith decimation. Calculations have been carried out and formulas have been derived that implement digital filtering using the WM with downsampling. The derived formulas can be used for 1D filtering with an arbitrary downsampling stride. Hardware modeling of wavelet image filtering on an FPGA showed that the WM reduces the computational time by up to 66%, with increases in the hardware costs and power consumption of 95% and 344%, respectively, compared to the direct method. A promising direction for further research is the implementation of the developed approach on ASIC and the use of modular computing for more efficient parallelization of calculations and an even greater increase in the device speed....
Medical intelligence detection systems have changed with the help of artificial intelligence and have also faced challenges. Breast cancer diagnosis and classification are part of this medical intelligence system. Early detection can lead to an increase in treatment options. On the other hand, uncertainty is a case that has always been with the decision-maker. The system’s parameters cannot be accurately estimated, and the wrong decision is made. To solve this problem, we have proposed a method in this article that reduces the ignorance of the problem with the help of Dempster–Shafer theory so that we can make a better decision. This research on the MIAS dataset, based on image processing machine learning and Dempster–Shafer mathematical theory, tries to improve the diagnosis and classification of benign, malignant masses. We first determine the results of the diagnosis of mass type with MLP by using the texture feature and CNN. We combine the results of the two classifications with Dempster–Shafer theory and improve its accuracy. The obtained results show that the proposed approach has better performance than others based on evaluation criteria such as accuracy of 99.10%, sensitivity of 98.4%, and specificity of 100%....
Data-hunger is a persistent challenge in machine learning, particularly in the field of image processing based on convolutional neural networks (CNNs). This study systematically investigates the factors contributing to data-hunger in machine-learningbased image-processing algorithms. The results revealed that the proliferation of model parameters, the lack of interpretability, and the complexity of model structure are significant factors influencing data-hunger. Based on these findings, this paper introduces a novel semi-white-box neural network model construction strategy. This approach effectively reduces the number of model parameters while enhancing the interpretability of model components. It accomplishes this by constraining uninterpretable processes within the model and leveraging prior knowledge of image processing for model. Rather than relying on a single allin- one model, a semi-white-box model is composed of multiple smaller models, each responsible for extracting fundamental semantic features. The final output is derived from these features and prior knowledge. The proposed strategy holds the potential to substantially decrease data requirements under specific data source conditions while improving the interpretability of model components. Validation experiments are conducted on well-established datasets, including MNIST, Fashion MNIST, CIFAR, and generated data. The results demonstrate the superiority of the semi-white-box strategy over the traditional all-in-one approach in terms of accuracy when trained with equivalent data volumes. Impressively, on the tested datasets, a simplified semi-white-box model achieves performance close to that of ResNet while utilizing a small number of parameters. Furthermore, the semiwhite- box strategy offers improved interpretability and parameter reusability features that are challenging to achieve with the allin- one approach. In conclusion, this paper contributes to mitigating data-hunger challenges in machine-learning-based image processing through the introduction of a novel semi-white-box model construction strategy, backed by empirical evidence of its effectiveness....
The application of geomatics to the agroforestry field is acquiring greater prominence in recent times in a world that is increasingly digital and aware of sustainability and food security. The use of geomatics tools for 3D documentation and visualisation is becoming essential in so-called precision agriculture. This article describes the methodology used to obtain the wood volume of a set of vines in the town of Tarazona de la Mancha (Albacete, Spain)....
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