Pub Date : 2021-06-08DOI: 10.1109/CYBCONF51991.2021.9464132
Kaito Furuo, Kento Morita, Tomohito Hagi, Tomoki Nakamura, T. Wakabayashi
The bone tumor causes the bone pain and swelling, and is firstly diagnosed in a local hospital in many cases. This has become a problem in recent years, and also the benign and malignant nature of bone tumors is difficult and requires a great deal of effort even for medical specialists. Therefore, the development of a system to automatically estimate the benign or malignant nature of bone tumors is required. In this study, we propose a method for automatically estimating the benignity or malignancy of bone tumors using deep learning. We fine-tuned VGG16 and ResNet152 trained on ImageNet using image patches extracted from 38 plain X-ray images of 3 patients. Results on patch-level classification showed that VGG16 achieved higher estimation accuracy (f1-score of 0.790) than ResNet152 (f1-score of 0.784). We also performed the tumor-level classification experiment in which 4 benign and 6 malignant tumors were correctly classified to the appropriate class.
{"title":"Automatic benign and malignant estimation of bone tumors using deep learning","authors":"Kaito Furuo, Kento Morita, Tomohito Hagi, Tomoki Nakamura, T. Wakabayashi","doi":"10.1109/CYBCONF51991.2021.9464132","DOIUrl":"https://doi.org/10.1109/CYBCONF51991.2021.9464132","url":null,"abstract":"The bone tumor causes the bone pain and swelling, and is firstly diagnosed in a local hospital in many cases. This has become a problem in recent years, and also the benign and malignant nature of bone tumors is difficult and requires a great deal of effort even for medical specialists. Therefore, the development of a system to automatically estimate the benign or malignant nature of bone tumors is required. In this study, we propose a method for automatically estimating the benignity or malignancy of bone tumors using deep learning. We fine-tuned VGG16 and ResNet152 trained on ImageNet using image patches extracted from 38 plain X-ray images of 3 patients. Results on patch-level classification showed that VGG16 achieved higher estimation accuracy (f1-score of 0.790) than ResNet152 (f1-score of 0.784). We also performed the tumor-level classification experiment in which 4 benign and 6 malignant tumors were correctly classified to the appropriate class.","PeriodicalId":231194,"journal":{"name":"2021 5th IEEE International Conference on Cybernetics (CYBCONF)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126032235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-08DOI: 10.1109/CYBCONF51991.2021.9464140
M. Fukumoto, Gan Haoran, Y. Hanada
Obtaining media content suited to user’s feelings is one of the essential targets of engineering. However, it is still difficult because feelings are different between users and are hard to be shown as a certain equation. As a method of a beverage, this study proposed Interactive Tabu Search (ITS) that blends source juices for creating new beverages suited to each user’s feelings. Tabu Search is one of stochastic local searches, and its properties are a continuous neighborhood search and a tabu list prohibiting cycling. A target of optimization was the ratio of the source juices. A concrete system based on the proposed ITS was constructed with the computer, Arduino, and peristaltic pumps. A tasting experiment composed of two steps was conducted. The target was delicious blended beverage. As a result, continuous increases in the fitness values related to deliciousness were observed, and a significant increase was observed in the maximum fitness. In the progress of the ratios, both different and common trends between the subjects were observed.
{"title":"A Proposal of Interactive Tabu Search for Creating Beverage by Blending Source Juices","authors":"M. Fukumoto, Gan Haoran, Y. Hanada","doi":"10.1109/CYBCONF51991.2021.9464140","DOIUrl":"https://doi.org/10.1109/CYBCONF51991.2021.9464140","url":null,"abstract":"Obtaining media content suited to user’s feelings is one of the essential targets of engineering. However, it is still difficult because feelings are different between users and are hard to be shown as a certain equation. As a method of a beverage, this study proposed Interactive Tabu Search (ITS) that blends source juices for creating new beverages suited to each user’s feelings. Tabu Search is one of stochastic local searches, and its properties are a continuous neighborhood search and a tabu list prohibiting cycling. A target of optimization was the ratio of the source juices. A concrete system based on the proposed ITS was constructed with the computer, Arduino, and peristaltic pumps. A tasting experiment composed of two steps was conducted. The target was delicious blended beverage. As a result, continuous increases in the fitness values related to deliciousness were observed, and a significant increase was observed in the maximum fitness. In the progress of the ratios, both different and common trends between the subjects were observed.","PeriodicalId":231194,"journal":{"name":"2021 5th IEEE International Conference on Cybernetics (CYBCONF)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123575214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-08DOI: 10.1109/CYBCONF51991.2021.9464153
Yuzi Jiang, Danilo Vasconcellos Vargas
Subpopulation algorithm based on novelty (SAN) has been investigated for some time and proved that it can be used for multi-objective optimization problems. It outperforms subpopulation algorithm based on general differential evolution (SAGDE) under the same framework, which highlights its special intrinsic mechanism. This intrinsic mechanism has something in common with some state-of-the-art multi-objective optimization algorithms. However, SAN has not yet proved its ability to be better than these algorithms and has not proven its ability to optimize problems with more than 5 objectives. In this paper, the advantage of SAN over other subpopulation algorithms, i.e., novelty search, is presented in detail. The similarities and differences between the intrinsic mechanisms of SAN, nondominated sorting genetic algorithm series (NSGAs) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) are also analyzed. Finally, these three algorithms are evaluated on several well-known benchmark problems with more than two objectives. The result shows SAN surpassed NSGA-III (latest version in NSGAs) in 20 out of the 32 problems, surpassed MOEA/D in 26 problems in 10 runs, which preliminary proved it surpasses the State-of-the-Art.
{"title":"Preliminary Results for Subpopulation Algorithm Based on Novelty (SAN) Compared with the State of the Art","authors":"Yuzi Jiang, Danilo Vasconcellos Vargas","doi":"10.1109/CYBCONF51991.2021.9464153","DOIUrl":"https://doi.org/10.1109/CYBCONF51991.2021.9464153","url":null,"abstract":"Subpopulation algorithm based on novelty (SAN) has been investigated for some time and proved that it can be used for multi-objective optimization problems. It outperforms subpopulation algorithm based on general differential evolution (SAGDE) under the same framework, which highlights its special intrinsic mechanism. This intrinsic mechanism has something in common with some state-of-the-art multi-objective optimization algorithms. However, SAN has not yet proved its ability to be better than these algorithms and has not proven its ability to optimize problems with more than 5 objectives. In this paper, the advantage of SAN over other subpopulation algorithms, i.e., novelty search, is presented in detail. The similarities and differences between the intrinsic mechanisms of SAN, nondominated sorting genetic algorithm series (NSGAs) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) are also analyzed. Finally, these three algorithms are evaluated on several well-known benchmark problems with more than two objectives. The result shows SAN surpassed NSGA-III (latest version in NSGAs) in 20 out of the 32 problems, surpassed MOEA/D in 26 problems in 10 runs, which preliminary proved it surpasses the State-of-the-Art.","PeriodicalId":231194,"journal":{"name":"2021 5th IEEE International Conference on Cybernetics (CYBCONF)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131228890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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