Pub Date : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452942
M. Raveendra, U. Saravanakumar, G. Kumar, P. Suresh, Saam Prasanth Dheeraj Pedapalli
In this work, a multiple triangular-slot Substrate Integrated Waveguide (SIW) antenna has been proposed for 5th generation mobile communication applications with broadband characteristics. The proposed siw antenna has been realized with three different structures to satisfy the millimeter-wavelength for 5G mobile communication applications on the frequency spectrum. Copper vias have been integrated between ground and patch surfaces, later to introduce triangular slots on the patch element to obtain broadband characteristics with low return loss performance. The Rogers RO4232 (tm) substrate material is used with dielectric relative permittivity 3.2, loss tangent 0.0018 and the thickness of the dielectric medium is 1.6 mm. This design has been simulated on HFSS software. The performance of the antenna is analyzed with the help of the characteristics of return loss, VSWR, bandwidth and its radiation patterns properties. The designed SIW antenna offers a resonating frequency band from 21.80 GHz to 37.34 GHz.
{"title":"A Broadband Millimeter-Wave SIW Antenna for 5G Mobile Communication","authors":"M. Raveendra, U. Saravanakumar, G. Kumar, P. Suresh, Saam Prasanth Dheeraj Pedapalli","doi":"10.1109/ICOEI51242.2021.9452942","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452942","url":null,"abstract":"In this work, a multiple triangular-slot Substrate Integrated Waveguide (SIW) antenna has been proposed for 5th generation mobile communication applications with broadband characteristics. The proposed siw antenna has been realized with three different structures to satisfy the millimeter-wavelength for 5G mobile communication applications on the frequency spectrum. Copper vias have been integrated between ground and patch surfaces, later to introduce triangular slots on the patch element to obtain broadband characteristics with low return loss performance. The Rogers RO4232 (tm) substrate material is used with dielectric relative permittivity 3.2, loss tangent 0.0018 and the thickness of the dielectric medium is 1.6 mm. This design has been simulated on HFSS software. The performance of the antenna is analyzed with the help of the characteristics of return loss, VSWR, bandwidth and its radiation patterns properties. The designed SIW antenna offers a resonating frequency band from 21.80 GHz to 37.34 GHz.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121920691","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452991
A. Srujan, R. Srija, Suraj Sara, S. Sahithi, V. Krishna, A. M. Baradwaj
This paper deals with the detection of cancer from gastrointestinal images. Cancer detection is the most adequate field of implementation in bio-medical domains. At first, the several capabilities have been recognized to automate the process of identification of cancer and also upscale the accuracy rates over alternative diagnostic techniques. The methods that presently exist to diagnose cancer are not working constructively on all kinds of images, especially poor-quality images such as images with too much noise. And also, most of the available techniques have completely ignored the effective use of object segmentation in gastrointestinal images. So, to subdue the limitations of previous techniques, a new approach has been proposed in this paper. Impressive results have been generated by using the features of image processing in MATLAB with the help of images from kvasir dataset. The image processing techniques used for diagnostic test pictures might facilitate the sight of distinctive options in cancer detection.
{"title":"Detecting Cancer in Gastrointestinal Images using MATLAB","authors":"A. Srujan, R. Srija, Suraj Sara, S. Sahithi, V. Krishna, A. M. Baradwaj","doi":"10.1109/ICOEI51242.2021.9452991","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452991","url":null,"abstract":"This paper deals with the detection of cancer from gastrointestinal images. Cancer detection is the most adequate field of implementation in bio-medical domains. At first, the several capabilities have been recognized to automate the process of identification of cancer and also upscale the accuracy rates over alternative diagnostic techniques. The methods that presently exist to diagnose cancer are not working constructively on all kinds of images, especially poor-quality images such as images with too much noise. And also, most of the available techniques have completely ignored the effective use of object segmentation in gastrointestinal images. So, to subdue the limitations of previous techniques, a new approach has been proposed in this paper. Impressive results have been generated by using the features of image processing in MATLAB with the help of images from kvasir dataset. The image processing techniques used for diagnostic test pictures might facilitate the sight of distinctive options in cancer detection.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116020284","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 : 2021-06-03DOI: 10.1109/icoei51242.2021.9453018
{"title":"Index Author","authors":"","doi":"10.1109/icoei51242.2021.9453018","DOIUrl":"https://doi.org/10.1109/icoei51242.2021.9453018","url":null,"abstract":"","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"67 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125843877","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452864
Jithina Jose, J. Vimali, P. Ajitha, S. Gowri, A. Sivasangari, Bevish Y. Jinila
These days, drowsy driving plays a significant role in a lot of road incidents. Car accidents can be avoided by implementing a system with alarm to alert drowsy drivers in order to focus on the road and help them to stay focused. This paper has developed to detect driver drowsiness and trigger them with an alarm to alert drivers in order to prevent accidents, and reduce loss of lives and sufferings. Several techniques have been studied and analyzed to conclude the best technique with highest accuracy to detect the driver drowsiness. The proposed method utilizes Python, dlib, and OpenCV to build a real-time framework that uses a computerized camera to monitor and process the driver's eye and yawn. A camera will be utilized so that it concentrates towards monitoring the driver's eye and yawn. A trigger is issued to alert the driver. The proposed system acknowledges whether thedriver is sleepy and it gives a caution alert, when his eyes and yawn are discovered close together for a particular measure of casing.
{"title":"Drowsiness Detection System for Drivers Using Image Processing Technique","authors":"Jithina Jose, J. Vimali, P. Ajitha, S. Gowri, A. Sivasangari, Bevish Y. Jinila","doi":"10.1109/ICOEI51242.2021.9452864","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452864","url":null,"abstract":"These days, drowsy driving plays a significant role in a lot of road incidents. Car accidents can be avoided by implementing a system with alarm to alert drowsy drivers in order to focus on the road and help them to stay focused. This paper has developed to detect driver drowsiness and trigger them with an alarm to alert drivers in order to prevent accidents, and reduce loss of lives and sufferings. Several techniques have been studied and analyzed to conclude the best technique with highest accuracy to detect the driver drowsiness. The proposed method utilizes Python, dlib, and OpenCV to build a real-time framework that uses a computerized camera to monitor and process the driver's eye and yawn. A camera will be utilized so that it concentrates towards monitoring the driver's eye and yawn. A trigger is issued to alert the driver. The proposed system acknowledges whether thedriver is sleepy and it gives a caution alert, when his eyes and yawn are discovered close together for a particular measure of casing.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124002033","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452896
F M Javed Mehedi Shamrat, Md. Al Jubair, M. Billah, Sovon Chakraborty, M. Alauddin, Rumesh Ranjan
Deep learning is a trendy term these days, and it refers to a modern age in machine learning in which algorithms are taught to identify patterns in vast amounts of data. It mostly refers to studying various layers of representation, which assists in the understanding of data that includes text, sound, and pictures. To interact with the objects in a video series, many researchers use a form of deep learning called a CNN. Face detection involves several face-related technologies, such as face authentication, facial recognition, and face clustering. For identification and understanding, effective preparation must be carried out. The standard technique did not produce a positive outcome in terms of face recognition precision. The objectives of this research are by using a deep learning model to enhance the accuracy of face detection. For recognizing faces from datasets, the proposed model utilizes a deep learning technique named convolutional neural networks. The proposed work is applied using Max Pooling, a well-known deep learning process. Our model is trained and validated using the LFW dataset, which includes 13000 photos collected from Kaggle. The training accuracy of the model was 95.72% percent, and the validation accuracy was 96.27%.
{"title":"A Deep Learning Approach for Face Detection using Max Pooling","authors":"F M Javed Mehedi Shamrat, Md. Al Jubair, M. Billah, Sovon Chakraborty, M. Alauddin, Rumesh Ranjan","doi":"10.1109/ICOEI51242.2021.9452896","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452896","url":null,"abstract":"Deep learning is a trendy term these days, and it refers to a modern age in machine learning in which algorithms are taught to identify patterns in vast amounts of data. It mostly refers to studying various layers of representation, which assists in the understanding of data that includes text, sound, and pictures. To interact with the objects in a video series, many researchers use a form of deep learning called a CNN. Face detection involves several face-related technologies, such as face authentication, facial recognition, and face clustering. For identification and understanding, effective preparation must be carried out. The standard technique did not produce a positive outcome in terms of face recognition precision. The objectives of this research are by using a deep learning model to enhance the accuracy of face detection. For recognizing faces from datasets, the proposed model utilizes a deep learning technique named convolutional neural networks. The proposed work is applied using Max Pooling, a well-known deep learning process. Our model is trained and validated using the LFW dataset, which includes 13000 photos collected from Kaggle. The training accuracy of the model was 95.72% percent, and the validation accuracy was 96.27%.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126556184","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9452980
Sree Lakshmi K, Theertha Jayarajan N, Nitha L
Data flows from various sources in structured, semistructured or unstructured form and this type of data flow is referred as big data. Due to their large scale, rapid growth and diverse formats, these datasets are difficult to manage using conventional tools and techniques. Big Data analysis is a daunting activity as it requires large decentralized file systems that should be adaptive, resilient and responsive to fault. For the effective analysis of big data, Map Reduce is commonly used. Big data analysis helps researchers, scholars, and business users to extract the value and knowledge. Huge amounts of data have become accessible to decision makers in the information age. Due to the rapid increase of such data, strategies to manage and obtain value and knowledge from these datasets must be studied and delivered. Moreover, decision-makers must be able to extract useful information from such a dynamic and rapidly changing set of data, which includes everything from daily transactions to customer contact and social media data. In this paper, we explore Hadoop's parallel processing power in two application areas. The first scenario is calculation of minimum and maximum temperature with huge amount of weather data, which has been collected from an open source. The application analyses the entire weather station data set and the minimum and maximum temperatures (in Fahrenheit) of the respective weather stations will be displayed. The second scenario is to find the word count from huge datasets and checks the frequency of each word in a given data set irrespective of the data volume.
{"title":"Ascendancy of MapReduce with Hadoop for Weather Data and Word Count Analytics","authors":"Sree Lakshmi K, Theertha Jayarajan N, Nitha L","doi":"10.1109/ICOEI51242.2021.9452980","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9452980","url":null,"abstract":"Data flows from various sources in structured, semistructured or unstructured form and this type of data flow is referred as big data. Due to their large scale, rapid growth and diverse formats, these datasets are difficult to manage using conventional tools and techniques. Big Data analysis is a daunting activity as it requires large decentralized file systems that should be adaptive, resilient and responsive to fault. For the effective analysis of big data, Map Reduce is commonly used. Big data analysis helps researchers, scholars, and business users to extract the value and knowledge. Huge amounts of data have become accessible to decision makers in the information age. Due to the rapid increase of such data, strategies to manage and obtain value and knowledge from these datasets must be studied and delivered. Moreover, decision-makers must be able to extract useful information from such a dynamic and rapidly changing set of data, which includes everything from daily transactions to customer contact and social media data. In this paper, we explore Hadoop's parallel processing power in two application areas. The first scenario is calculation of minimum and maximum temperature with huge amount of weather data, which has been collected from an open source. The application analyses the entire weather station data set and the minimum and maximum temperatures (in Fahrenheit) of the respective weather stations will be displayed. The second scenario is to find the word count from huge datasets and checks the frequency of each word in a given data set irrespective of the data volume.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793025","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453080
M. Guo
As an important part of English teaching, oral English evaluation plays an important role in promoting students to learn English. The establishment of a diversified oral college English evaluation system is conducive to changing the traditional summative evaluation model, promoting the smooth progress of college English reform, and facilitating the in-depth development of the overall education reform. Fuzzy measure theory abandons the requirement of additivity in classical measure theory. On the basis of the concept of generalized additivity, the condition of additivity is weakened to make it additive in the new sense. With the development of deep learning, speech recognition technology has undergone tremendous technological changes, in which the acoustic model has gradually developed from the traditional Gaussian mixture model to the neural network model. In this paper, the speech recognition technology and fuzzy measure rules are analyzed, and the evaluation system of spoken English is constructed.
{"title":"Oral English Evaluation Algorithm Based on Fuzzy Measures and Speech Recognition Technology","authors":"M. Guo","doi":"10.1109/ICOEI51242.2021.9453080","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453080","url":null,"abstract":"As an important part of English teaching, oral English evaluation plays an important role in promoting students to learn English. The establishment of a diversified oral college English evaluation system is conducive to changing the traditional summative evaluation model, promoting the smooth progress of college English reform, and facilitating the in-depth development of the overall education reform. Fuzzy measure theory abandons the requirement of additivity in classical measure theory. On the basis of the concept of generalized additivity, the condition of additivity is weakened to make it additive in the new sense. With the development of deep learning, speech recognition technology has undergone tremendous technological changes, in which the acoustic model has gradually developed from the traditional Gaussian mixture model to the neural network model. In this paper, the speech recognition technology and fuzzy measure rules are analyzed, and the evaluation system of spoken English is constructed.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126221013","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453068
Anjali Anil Kumar, Navya Lal, R. N. Kumar
Image processing is a fast growing area of active research. It comprises methods to perform several useful operations on images, to modify/enhance the image or to tease out useful information from it. A very basic application of image processing is image filtering. Filtering is a technique of image modification or enhancement. We filter an image to enhance some features or to get rid of other features - the techniques include smoothing, sharpening, edge enhancement. Here we apply different smoothing and edge enhancement filtering methods to an image and evaluate the quality of the image in both cases using an image quality assessment technique called BRISQUE and by calculating the PSNR ratio of images.
{"title":"A Comparative Study of Various Filtering Techniques","authors":"Anjali Anil Kumar, Navya Lal, R. N. Kumar","doi":"10.1109/ICOEI51242.2021.9453068","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453068","url":null,"abstract":"Image processing is a fast growing area of active research. It comprises methods to perform several useful operations on images, to modify/enhance the image or to tease out useful information from it. A very basic application of image processing is image filtering. Filtering is a technique of image modification or enhancement. We filter an image to enhance some features or to get rid of other features - the techniques include smoothing, sharpening, edge enhancement. Here we apply different smoothing and edge enhancement filtering methods to an image and evaluate the quality of the image in both cases using an image quality assessment technique called BRISQUE and by calculating the PSNR ratio of images.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"1933 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128779164","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453087
Mingdong Chen
With the rapid development of computer technology, geographic information system and remote sensing technology, the popularization of data information technology has been greatly promoted. Grassland, as an important part of natural resources, is increasingly managed by geographic information platform, including ground observation of grassland vegetation, remote sensing information data acquisition, positioning and navigation, and application of satellite remote sensing data. Grassland data acquisition provides scientific and technological means for the acquisition, processing, analysis, use and management of grassland vegetation and ecological information. At the same time, GIS platform can effectively integrate basic spatial database sharing, data services and applications, and significantly improve the development and application level of basic geospatial data.
{"title":"Grassland Data Acquisition based on Internet of Things and Cloud Computing","authors":"Mingdong Chen","doi":"10.1109/ICOEI51242.2021.9453087","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453087","url":null,"abstract":"With the rapid development of computer technology, geographic information system and remote sensing technology, the popularization of data information technology has been greatly promoted. Grassland, as an important part of natural resources, is increasingly managed by geographic information platform, including ground observation of grassland vegetation, remote sensing information data acquisition, positioning and navigation, and application of satellite remote sensing data. Grassland data acquisition provides scientific and technological means for the acquisition, processing, analysis, use and management of grassland vegetation and ecological information. At the same time, GIS platform can effectively integrate basic spatial database sharing, data services and applications, and significantly improve the development and application level of basic geospatial data.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129144371","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 : 2021-06-03DOI: 10.1109/ICOEI51242.2021.9453079
M.V. Sowmya Lakshmi, P. L. Saisreeja, L. Chandana, P. Mounika, P. U
Brain Tumor identification has been regarded as a critical topic. Meanwhile, it is complicated to spot the tumor in MRI images manually from a large amount of MRI images generated is difficult and time-consuming due to unpredictable shapes and sizes of the tumor. Image Segmentation techniques make a massive impact here and help in obtaining more significant results by dividing the image into segments for prior identification of tumors. U-Net with LeakyReLu can be used for faster and precise segmentation of medical images. Thresholding is applied to identify the ROI of the tumor for better identification of the abnormality of the tumor. Identifying the tumor region from the segmented MRI image is lesser time-consuming. Therefore, our model developed using neural networks can help the doctors in precisely identifying the tumor region from the segmented images and thereby assisting them to help the patients.
{"title":"A LeakyReLU based Effective Brain MRI Segmentation using U-NET","authors":"M.V. Sowmya Lakshmi, P. L. Saisreeja, L. Chandana, P. Mounika, P. U","doi":"10.1109/ICOEI51242.2021.9453079","DOIUrl":"https://doi.org/10.1109/ICOEI51242.2021.9453079","url":null,"abstract":"Brain Tumor identification has been regarded as a critical topic. Meanwhile, it is complicated to spot the tumor in MRI images manually from a large amount of MRI images generated is difficult and time-consuming due to unpredictable shapes and sizes of the tumor. Image Segmentation techniques make a massive impact here and help in obtaining more significant results by dividing the image into segments for prior identification of tumors. U-Net with LeakyReLu can be used for faster and precise segmentation of medical images. Thresholding is applied to identify the ROI of the tumor for better identification of the abnormality of the tumor. Identifying the tumor region from the segmented MRI image is lesser time-consuming. Therefore, our model developed using neural networks can help the doctors in precisely identifying the tumor region from the segmented images and thereby assisting them to help the patients.","PeriodicalId":420826,"journal":{"name":"2021 5th International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124409931","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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