An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

Narasimhulu, C. Venkata

doi:10.1049/ipr2.12179

Cited by 12 publications

(11 citation statements)

References 29 publications

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“…The experimental results of the algorithm show that it has the ability to protect the corresponding image and video details, but it has certain defects in the image and video processing speed and efficiency. Relevant research institutions [25,26] in the United States have proposed a switching filtering algorithm for the above problems, which classifies the corresponding impulse noise into noise pixels and normal pixels and processes the weighting function based on this. To a certain extent, this method has the advantages of protecting image edge details and improving processing efficiency.…”

Section: Related Research: Analysis Of Research Status Of Image Acqui...mentioning

confidence: 99%

Design of Image Acquisition System Based on Visual Intelligent Sensor and Video Signal Processing

Zhou

2022

Journal of Sensors

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With the rapid development and iteration of CMOS image sensor and other related technologies, in addition to the more advanced technology, the corresponding sensor also integrates the image signal processing system, so it is a leap for the image acquisition system. Based on this, this paper will take the visual intelligent sensor produced and developed by a semiconductor company as the hardware to complete the hardware design of the image acquisition and transmission system. In addition to the core processor of the system, this paper will also use audio and video processing chips and image data processing chips for collaborative analysis, so as to ensure the processing capacity and processing quality of the image acquisition system at the hardware design level. Compared with the traditional system, at the corresponding system software level, in order to further improve the quality of video signal processing, this paper creatively proposes a switch weighted median filter denoising algorithm, which has a significant impact on the processing of random impulse noise in video and the overall image filtering. At the same time, the algorithm also protects the edge details of the video image, thus improving the authenticity of the image and video. Based on this, this paper has built a verification platform based on supporting hardware and corresponding algorithms. Through hardware debugging and system operation analysis, when the operating frequency of the visual intelligent sensor is 100 MHz, the set acquisition depth is 10 bits, and the corresponding system collects data at a rate of 251 fps per second. In terms of the corresponding performance, the iteration number of the algorithm proposed in this paper is about 40% more than that of the traditional algorithm in unit time. In terms of the corresponding algorithm performance, the algorithm proposed in this paper is about 30% higher than the traditional algorithm. In terms of the efficiency of the corresponding image and video acquisition system, the algorithm proposed in this paper is about 44% higher than the traditional algorithm. Based on this, the system proposed in this paper has obvious advantages over the traditional algorithm.

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Section: Related Research: Analysis Of Research Status Of Image Acqui...mentioning

confidence: 99%

Design of Image Acquisition System Based on Visual Intelligent Sensor and Video Signal Processing

Zhou

2022

Journal of Sensors

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“…Random Forest is an ensemble learning method that combines multiple decision trees. We construct a Random Forest classi er with varying tree depths and the number of estimators to harness the strengths of ensemble techniques [42].…”

Section: Random Forestmentioning

confidence: 99%

Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction

Hamza,

Mezl

2024

Preprint

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Background Breast cancer remains a significant global health challenge, demanding accurate and effective diagnostic methods for timely treatment. Ultrasound imaging stands out as a valuable diagnostic tool for breast cancer due to its affordability, accessibility, and non-ionizing radiation properties. Methods We evaluate the proposed method using a publicly available breast ultrasound images. This paper introduces a novel approach to classifying breast ultrasound images based on segmentation and feature extraction algorithm. The proposed methodology involves several key steps. Firstly, breast ultrasound images undergo preprocessing to enhance image quality and eliminate potential noise. Subsequently, a U-Net + + is applied for the segmentation. A classification model is then trained and validated after extracting features by using Mobilenetv2 and Inceptionv3 of segmented images. This model utilizes modern machine learning and deep learning techniques to distinguish between malignant and benign breast masses. Classification performance is assessed using quantitative metrics, including recall, precision and accuracy. Our results demonstrate improved precision and consistency compared to classification approaches that do not incorporate segmentation and feature extraction. Feature extraction using InceptionV3 and MobileNetV2 showed high accuracy, with MobileNetV2 outperforming InceptionV3 across various classifiers. Results The ANN classifier, when used with MobileNetV2, demonstrated a significant increase in test accuracy (0.9658) compared to InceptionV3 (0.7280). In summary, our findings suggest that the integration of segmentation techniques and feature extraction has the potential to enhance classification algorithms for breast cancer ultrasound images. Conclusion This approach holds promise for supporting radiologists, enhancing diagnostic accuracy, and ultimately improving outcomes for breast cancer patients. In future our focus will be to use comprehensive datasets to validate our methodology.

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“…Narasimhulu 29 have presented an automatic feature selection and classification mode for examining ultrasound kidney images utilizing dragonfly algorithm with random forest classifier. Preprocessing was the first identification mode to upgrade image superiority, imageries were prepared to secure the interest pixels before extracting the feature.…”

Section: Literature Surveymentioning

confidence: 99%

Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm for kidney stone detection

V.¹,

K.²,

Rooban

et al. 2023

Concurrency and Computation

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Summary Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm (AOA) is proposed in this manuscript for kidney stone detection. Initially, the input kidney stone images are gathered via CT kidney dataset. Then, the input image is preprocessed by utilizing APPDRC filtering approach. Also, the preprocessed images are given to the multi‐level thresholding segmentation technique for segmented the image. Then the segmented images are given to the TF‐IDF feature extraction method for extracting the features. Then the extracted features are fed to feature selection using Weibull distributive generalized multidimensional scaling methods for selecting the features. Then the selected features are SPGGAN classification method for kidney stone detection. Generally, SPGGAN not reveal any adoption of optimization methods compute optimum parameters for assuring correct kidney stone detection. Thus, AOA is used for optimizing the weight parameters of SPGGAN and it is implemented in python, its performance is examined under certain performances metrics, such as accuracy, precision, sensitivity, specificity, F‐measure, computational time, and ROC. The proposed SPGGAN‐AOA‐KSD method attains 54.78%, 34.89%, and 20.96% higher accuracy and 3.45%, 4.08%, and 5.06% greater AUC compared with existing methods, such as a ANN‐OGGA‐KSD, HMANN‐BPA‐KSD, and ANN‐CSOA‐KSD respectively.

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An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

Cited by 12 publications

References 29 publications

Design of Image Acquisition System Based on Visual Intelligent Sensor and Video Signal Processing

Design of Image Acquisition System Based on Visual Intelligent Sensor and Video Signal Processing

Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction

Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm for kidney stone detection

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