The detection and identification of cancerous tissue is currently a time-consuming and challenging process. The segmentation of liver lesions from cancer CT images can aid in treatment planning and clinical response monitoring. This study employs Residual U-Net, a powerful tool that has been adapted and applied for the segmentation of liver tumors, addressing the ongoing challenge in liver cancer diagnosis. Segmentation of liver lesions in CT images can be utilized to assess tumor burden, predict therapeutic outcomes, and monitor clinical response. In this research, the liver was extracted from the CT image using ResUNet, and the tumor was subsequently segmented using another ResUNet applied to the extracted Region of Interest (ROI). This approach effectively extracts features from Inception by combining residual and pre-trained weights. The deep learning system elucidates the underlying concept by highlighting the components contributing to the inner layer analysis and prediction, and by revealing a section of the decision-making process employed by pre-trained deep neural networks.
{"title":"Experimental Investigations to Detection of Liver Cancer Using ResUNet","authors":"Koteswara Rao Kodepogu, Sandhya Rani Muthineni, Charisma Kethineedi, Jasthi Tejesh, Joshitha Sai Uppalapati","doi":"10.18280/ts.400548","DOIUrl":"https://doi.org/10.18280/ts.400548","url":null,"abstract":"The detection and identification of cancerous tissue is currently a time-consuming and challenging process. The segmentation of liver lesions from cancer CT images can aid in treatment planning and clinical response monitoring. This study employs Residual U-Net, a powerful tool that has been adapted and applied for the segmentation of liver tumors, addressing the ongoing challenge in liver cancer diagnosis. Segmentation of liver lesions in CT images can be utilized to assess tumor burden, predict therapeutic outcomes, and monitor clinical response. In this research, the liver was extracted from the CT image using ResUNet, and the tumor was subsequently segmented using another ResUNet applied to the extracted Region of Interest (ROI). This approach effectively extracts features from Inception by combining residual and pre-trained weights. The deep learning system elucidates the underlying concept by highlighting the components contributing to the inner layer analysis and prediction, and by revealing a section of the decision-making process employed by pre-trained deep neural networks.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"151 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimation of Forest Diameter-at-Breast-Height: A Fusion of Machine Learning and 3D Image Processing Innovations","authors":"Yichen Wang, Jiyu Sun, Fangyu Wang","doi":"10.18280/ts.400547","DOIUrl":"https://doi.org/10.18280/ts.400547","url":null,"abstract":"ABSTRACT","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"37 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, a device, augmented by artificial intelligence and controlled by Raspberry Pi, has been engineered for estimating the yield of walnut trees and assessing walnut quality. The device, equipped with a camera, identifies walnuts in real-time using the YOLO V5 detection system. For each detected image of a walnut, feature extraction, selection, and classification were conducted employing a Support Vector Machine (SVM). This methodology facilitated the development of a system capable of determining and recording the quality of all walnuts within a tree or orchard. By leveraging deep neural networks for the analysis of 1800 walnut samples, the device demonstrated an accuracy of 98% in ascertaining walnut quality. This innovative device holds the capacity to swiftly analyze a considerable quantity of walnuts, thereby providing a numerical representation of the quality classes of walnuts cultivated by growers. This quantitative evaluation of walnut quality could subsequently streamline agricultural activities such as irrigation and fertilization, enabling a more efficient and informed approach to these processes. The findings presented in this study underscore the potential of integrating artificial intelligence with practical devices for enhancing the productivity and quality control in agriculture.
{"title":"A Raspberry Pi-Guided Device Using an Ensemble Convolutional Neural Network for Quantitative Evaluation of Walnut Quality","authors":"Turab Selçuk, Mustafa Nuri Tütüncü","doi":"10.18280/ts.400546","DOIUrl":"https://doi.org/10.18280/ts.400546","url":null,"abstract":"In this study, a device, augmented by artificial intelligence and controlled by Raspberry Pi, has been engineered for estimating the yield of walnut trees and assessing walnut quality. The device, equipped with a camera, identifies walnuts in real-time using the YOLO V5 detection system. For each detected image of a walnut, feature extraction, selection, and classification were conducted employing a Support Vector Machine (SVM). This methodology facilitated the development of a system capable of determining and recording the quality of all walnuts within a tree or orchard. By leveraging deep neural networks for the analysis of 1800 walnut samples, the device demonstrated an accuracy of 98% in ascertaining walnut quality. This innovative device holds the capacity to swiftly analyze a considerable quantity of walnuts, thereby providing a numerical representation of the quality classes of walnuts cultivated by growers. This quantitative evaluation of walnut quality could subsequently streamline agricultural activities such as irrigation and fertilization, enabling a more efficient and informed approach to these processes. The findings presented in this study underscore the potential of integrating artificial intelligence with practical devices for enhancing the productivity and quality control in agriculture.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"435 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Secure Image Retrieval and Sharing Technologies for Digital Inclusive Finance: Methods and Applications","authors":"Wei Wang","doi":"10.18280/ts.400525","DOIUrl":"https://doi.org/10.18280/ts.400525","url":null,"abstract":"ABSTRACT","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"43 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136104820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The identification of plant diseases is one of the most essential and difficult concerns in agriculture, necessitating solutions with a brighter light. With the onset of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have aided farmers in identifying and classifying plant features with a high degree of intellectual precision. However, accurate disease classification in plants is essential for empowering farmers to cultivate more and produce more. This study therefore presents a unique assembly of attention, capsule, and feedforward classification layers for reaching the maximum classification accuracy for plant diseases. The proposed system uses user-defined customized Convolutional Transfer Learning networks (CTLN) to extract features and the attention networks exclude unnecessary features and highlight only critical features for classification. Finally, the selected characteristics are sent to the Feedforward Capsule networks to improve performance. This paper proposes a paradigm that overcomes the constraints of existing deep learning networks and drastically decreases the computing burden. The suggested network is thoroughly evaluated utilizing Plant Village databases containing over 50,000 photos of healthy and diseased plants. The performance metrics of the proposed method are evaluated and compared to those of other learning networks. Compared to previous models, experimental results indicate that the proposed model has a 99.8 percent accuracy rate, lending support to the new categorization method that benefits farmer well-being.
{"title":"A Capsule Attention Network for Plant Disease Classification","authors":"Ponugoti Kalpana, R. Anandan","doi":"10.18280/ts.400523","DOIUrl":"https://doi.org/10.18280/ts.400523","url":null,"abstract":"The identification of plant diseases is one of the most essential and difficult concerns in agriculture, necessitating solutions with a brighter light. With the onset of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have aided farmers in identifying and classifying plant features with a high degree of intellectual precision. However, accurate disease classification in plants is essential for empowering farmers to cultivate more and produce more. This study therefore presents a unique assembly of attention, capsule, and feedforward classification layers for reaching the maximum classification accuracy for plant diseases. The proposed system uses user-defined customized Convolutional Transfer Learning networks (CTLN) to extract features and the attention networks exclude unnecessary features and highlight only critical features for classification. Finally, the selected characteristics are sent to the Feedforward Capsule networks to improve performance. This paper proposes a paradigm that overcomes the constraints of existing deep learning networks and drastically decreases the computing burden. The suggested network is thoroughly evaluated utilizing Plant Village databases containing over 50,000 photos of healthy and diseased plants. The performance metrics of the proposed method are evaluated and compared to those of other learning networks. Compared to previous models, experimental results indicate that the proposed model has a 99.8 percent accuracy rate, lending support to the new categorization method that benefits farmer well-being.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"70 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136069599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Plant disease outbreaks have a profound impact on the agricultural sector, leading to substantial economic implications, compromised crop yields and quality, and potential food scarcity. Consequently, the development of effective disease prevention and management strategies is crucial. This study introduces a novel methodology employing deep learning for the identification and diagnosis of plant diseases, with a focus on mitigating the associated detrimental effects. In this investigation, Convolutional Neural Networks (CNNs) were utilized to devise a disease identification method applicable to three types of plant leaves - peppers (two classes), potato (three classes), and tomato (nine classes). Preprocessing techniques, including image resizing and data augmentation, were adopted to facilitate the analysis. Additionally, three distinct feature extraction methods - Haralick feature, Histogram of Gradient (HOG), and Local Binary Patterns (LBP) - were implemented. The Grey Wolf Optimization (GWO) technique was employed as a feature selection strategy to identify the most advantageous features. This approach diverges from traditional methodologies that solely rely on CNNs for feature extraction, instead extracting features from the dataset through multiple extractors and passing them to the GWO for selection, followed by CNN classification. The proposed method demonstrated high efficiency, with classification accuracies reaching up to 99.8% for pepper, 99.9% for potato, and 95.7% for tomato. This study thus provides a progressive shift in plant disease detection, offering promising potential for improving agricultural health management. In conclusion, the integration of deep learning and the Grey Wolf Optimization technique presents a compelling approach for plant disease detection, demonstrating high accuracy and efficiency. This research contributes a significant advancement
{"title":"Deep Learning and Grey Wolf Optimization Technique for Plant Disease Detection: A Novel Methodology for Improved Agricultural Health","authors":"Amenah Nazar Jabbar, Hakan Koyuncu","doi":"10.18280/ts.400515","DOIUrl":"https://doi.org/10.18280/ts.400515","url":null,"abstract":"Plant disease outbreaks have a profound impact on the agricultural sector, leading to substantial economic implications, compromised crop yields and quality, and potential food scarcity. Consequently, the development of effective disease prevention and management strategies is crucial. This study introduces a novel methodology employing deep learning for the identification and diagnosis of plant diseases, with a focus on mitigating the associated detrimental effects. In this investigation, Convolutional Neural Networks (CNNs) were utilized to devise a disease identification method applicable to three types of plant leaves - peppers (two classes), potato (three classes), and tomato (nine classes). Preprocessing techniques, including image resizing and data augmentation, were adopted to facilitate the analysis. Additionally, three distinct feature extraction methods - Haralick feature, Histogram of Gradient (HOG), and Local Binary Patterns (LBP) - were implemented. The Grey Wolf Optimization (GWO) technique was employed as a feature selection strategy to identify the most advantageous features. This approach diverges from traditional methodologies that solely rely on CNNs for feature extraction, instead extracting features from the dataset through multiple extractors and passing them to the GWO for selection, followed by CNN classification. The proposed method demonstrated high efficiency, with classification accuracies reaching up to 99.8% for pepper, 99.9% for potato, and 95.7% for tomato. This study thus provides a progressive shift in plant disease detection, offering promising potential for improving agricultural health management. In conclusion, the integration of deep learning and the Grey Wolf Optimization technique presents a compelling approach for plant disease detection, demonstrating high accuracy and efficiency. This research contributes a significant advancement","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"70 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136069598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep Learning-Based Standardized Evaluation and Human Pose Estimation: A Novel Approach to Motion Perception","authors":"Yuzhong Liu, Tianfan Zhang, Zhe Li, Lequan Deng","doi":"10.18280/ts.400549","DOIUrl":"https://doi.org/10.18280/ts.400549","url":null,"abstract":"ABSTRACT","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"234 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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