Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization

Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features

The gastrointestinal system contains the upper and lower gastrointestinal tracts. The main tasks of the gastrointestinal system are to break down food and convert it into essential elements that the body can benefit from and expel waste in the form of feces. If any organ is affected, it does not work well, which affects the body. Many gastrointestinal diseases, such as infections, ulcers, and benign and malignant tumors, threaten human life. Endoscopy techniques are the gold standard for detecting infected parts within the organs of the gastrointestinal tract. Endoscopy techniques produce videos that are converted into thousands of frames that show the disease's characteristics in only some frames. Therefore, this represents a challenge for doctors because it is a tedious task that requires time, effort, and experience. Computer-assisted automated diagnostic techniques help achieve effective diagnosis to help doctors identify the disease and give the patient the appropriate treatment. In this study, many efficient methodologies for analyzing endoscopy images for diagnosing gastrointestinal diseases were developed for the Kvasir dataset. The Kvasir dataset was classified by three pre-trained models: GoogLeNet, MobileNet, and DenseNet121. The images were optimized, and the gradient vector flow (GVF) algorithm was applied to segment the regions of interest (ROIs), isolating them from healthy regions and saving the endoscopy images as Kvasir-ROI. The Kvasir-ROI dataset was classified by the three pre-trained GoogLeNet, MobileNet, and DenseNet121 models. Hybrid methodologies (CNN-FFNN and CNN-XGBoost) were developed based on the GVF algorithm and achieved promising results for diagnosing disease based on endoscopy images of gastroenterology. The last methodology is based on fused CNN models and their classification by FFNN and XGBoost networks. The hybrid methodology based on the fused CNN features, called GoogLeNet-MobileNet-DenseNet121-XGBoost, achieved an AUC of 97.54%, accuracy of 97.25%, sensitivity of 96.86%, precision of 97.25%, and specificity of 99.48%.

An Improved YOLOv5-Based Underwater Object-Detection Framework

To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.

An integrated method for evaluating the energy-saving and economic operation of power systems with interval-valued intuitionistic fuzzy numbers

Chinese population is numerous. Energy resources are limited. The ownership of per capita resource is far lower than the world average level. China is in the process of industrialization and urbanization, but energy resources are consumed and environmental pollution is serious. The energy crisis and environmental protection has restricted our country economy development and social harmony. As a source of energy consumption and environmental pollution, power industry is one of the important fields of energy saving and emission reduction. The reasonable power dispatch is the breakthrough to reduce the energy consumption and environmental pollution. In this paper, we first introduce some operations on interval-valued intuitionistic fuzzy sets, such as Heronian mean (HM) operator and Dombi operations, etc., and further develop the induced interval-valued intuitionistic fuzzy Dombi weighted Heronian mean (I-IVIFDWHM) operator. We also establish some desirable properties of this operator, such as commutativity, idempotency and monotonicity. Then, we apply the I-IVIFDWHM operator to deal with the interval-valued intuitionistic fuzzy multiple attribute decision making (MADM) problems. Finally, an illustrative example for evaluating the energy-saving and economic operation of power systems is given to verify the developed approach.

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

At present, rotating machinery is widely used in all walks of life and has become the key equipment in many production processes. It is of great significance to strengthen the condition monitoring of rotating machinery, timely diagnose and eliminate faults to ensure the safe and efficient operation of rotating machinery and improve the economic benefits of enterprises. When the state of a rotating machine deteriorates, the thermal energy that is much more than its normal operation will be generated due to the increase in the friction between the components or other factors. Therefore, using the infrared thermal camera to collect the infrared thermal images of rotating machinery and judge the health status of rotating machinery by observing the temperature distribution in the thermal images is often more rapid and effective than other technologies. Nevertheless, after decades of development, the research achievements of infrared thermography (IRT) and its application in various industrial fields are numerous and complex, and there is a lack of systematic sorting and summary of the achievements in this field. Accordingly, this paper summarizes the development and application of IRT as a non-contact and non-invasive tool for equipment condition monitoring and fault diagnosis, and introduces the basic theory of IRT, image processing technology and fault diagnosis methods of rotating machinery in detail. Finally, the review is summarized and some future potential topics are proposed, which will make the subject easier for beginners and non-experts to understand.

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

Mineral identification is an important part of geological research. Traditional mineral identification methods heavily rely on the identification ability of the identifier and external instruments, and therefore require expensive labor expenditures and equipment capabilities. Deep learning-based mineral identification brings a new solution to the problem, which not only saves labor costs, but also reduces identification errors. However, the accuracy of existing recognition efforts is often affected by various factors such as Mohs hardness, color, picture scale, and especially light intensity. To reduce the impact of light intensity on recognition accuracy, we propose an efficient deep learning-based mineral recognition method using the luminance equalization algorithm. In this paper, we first propose a new algorithm combining histogram equalization (HE) and the Laplace algorithm, and use this algorithm to process the luminance of the identified samples, and finally use the YOLOv5 model to identify the samples. The experimental results show that our method achieves 95.6% accuracy for the identification of 50 common minerals, achieving a luminance equalization-based deep learning mineral identification method.

Kidney Tumor Segmentation Based on FR2PAttU-Net Model

The incidence rate of kidney tumors increases year by year, especially for some incidental small tumors. It is challenging for doctors to segment kidney tumors from kidney CT images. Therefore, this paper proposes a deep learning model based on FR2PAttU-Net to help doctors process many CT images quickly and efficiently and save medical resources. FR2PAttU-Net is not a new CNN structure but focuses on improving the segmentation effect of kidney tumors, even when the kidney tumors are not clear. Firstly, we use the R2Att network in the "U" structure of the original U-Net, add parallel convolution, and construct FR2PAttU-Net model, to increase the width of the model, improve the adaptability of the model to the features of different scales of the image, and avoid the failure of network deepening to learn valuable features. Then, we use the fuzzy set enhancement algorithm to enhance the input image and construct the FR2PAttU-Net model to make the image obtain more prominent features to adapt to the model. Finally, we used the KiTS19 data set and took the size of the kidney tumor as the category judgment standard to enhance the small sample data set to balance the sample data set. We tested the segmentation effect of the model at different convolution and depths, and we got scored a 0.948 kidney Dice and a 0.911 tumor Dice results in a 0.930 composite score, showing a good segmentation effect.