2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)最新文献_第7页

Detection of Colon Cancer Using Inception V3 and Ensembled CNN Model 基于Inception V3和集成CNN模型的结肠癌检测

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101654

I. J. Swarna, Emrana Kabir Hashi

{"title":"Detection of Colon Cancer Using Inception V3 and Ensembled CNN Model","authors":"I. J. Swarna, Emrana Kabir Hashi","doi":"10.1109/ECCE57851.2023.10101654","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101654","url":null,"abstract":"Colon cancer is one of the most prevalent types of cancer. Early diagnosis of colon cancer can lead to an increased chance of successful treatment with less cost. To speed up this process deep learning can provide very useful and effective approaches. In this thesis work, two types of models were developed to classify colon cells from image data - one is the transfer learning model where a deep network Inception V3 is used as the pre-trained model and the other one is an Ensembled model which combines predictions of three simple sequential CNN models. To develop these models, 10k images were used from the LC25000 dataset and a very small Warwick-QU dataset having only 165 images was used to provide new data for retraining and testing purposes. Both models achieved a high result for the first dataset with 99.4% and 99.95% accuracy respectively, where Inception V3 showed 94.545% accuracy on new data from Warwick-QU after retraining and Ensembled model showed 78.182% accuracy. This approach can be used in research in the field of early and effective detection of colon cancer with a larger amount of varying images and more preprocessing methods to reduce overfitting and to make the model perform well in various types of images.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125041084","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}

引用次数: 0

Non-Invasive Blood Glucose Measurement Device: Performance analysis of Diffused Reflectance method and Diffused Transmittance method using Near Infrared Light 无创血糖测量仪:近红外光漫反射法和漫反射透射法的性能分析

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101505

Tanvir Raihan Khan, Asif Mostofa, Mrinmoy Dey

{"title":"Non-Invasive Blood Glucose Measurement Device: Performance analysis of Diffused Reflectance method and Diffused Transmittance method using Near Infrared Light","authors":"Tanvir Raihan Khan, Asif Mostofa, Mrinmoy Dey","doi":"10.1109/ECCE57851.2023.10101505","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101505","url":null,"abstract":"Diabetes is a condition that develops when blood glucose, often known as blood sugar, is too high. A diabetic patient must constantly monitor his or her blood glucose level to keep it under control. In the commercial invasive approach, a patient must injure his body part to obtain a blood sample, which is uncomfortable for the patient and can increase the risk of infection. Blood glucose monitoring using a non-invasive technique can lessen discomfort. In this paper, we suggested a non-invasive blood glucose measuring technique that consists of a Near Infrared LED (940nm) and a photodetector to estimate blood glucose levels. In our work, we employed Near Infrared Light to assess blood glucose levels. Following the device's implementation, we compared the accuracies of both diffused reflectance method and diffused transmittance method to see which method is preferable. It was found that diffused transmittance method is the better one of the two. The results from both methods were also compared with a commercial invasive blood glucometer on the market. It is observed from Clarke Error Grid Analysis that, most of the test data from diffused transmittance method lies in Region A. We have also developed an app that can show the data from the devices on patients' smartphones.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123285965","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}

引用次数: 0

Automated Gastrointestinal Tract Image Segmentation Of Cancer Patient Using LeVit-UNet To Automate Radiotherapy 利用levi - unet自动化放疗对癌症患者胃肠道图像进行自动分割

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101574

Md. Jafril Alam, Sakib Zaman, P. C. Shill, Sujoy Kar, Md. Azizul Hakim

{"title":"Automated Gastrointestinal Tract Image Segmentation Of Cancer Patient Using LeVit-UNet To Automate Radiotherapy","authors":"Md. Jafril Alam, Sakib Zaman, P. C. Shill, Sujoy Kar, Md. Azizul Hakim","doi":"10.1109/ECCE57851.2023.10101574","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101574","url":null,"abstract":"Gastrointestinal(GI) tract cancer is a common type of cancer around the world. Cancer patients require radiotherapy as a part of a cancer diagnosis. To provide therapy in the cancer-affected GI tract, it needs to avoid the stomach and bowels because, in this case, the stomach and intestine are not cancer affected. It is ineffective to manually avoid the intestines and stomach and move the X-ray beam toward the cancer cell because it is a time-consuming, labor-intensive mechanism. Besides these issues, a patient feels uncomfortable while repeatedly X-ray beam is set manually. We implemented a deep learning-based automated medical image segmentation method using LeVit-UNet to overcome these issues. LeVit-UNet is a transformer-based architecture built using the Le Vit unit and CNN. The proposed system properly segments images into three classes: stomach, large, and small bowel. Three backbones of LeVit-UNet: Le Vit-128, Le Vit-192, Le Vit-384 were used in our research. Validation loss, dice score, and IOU were generated and recorded to evaluate all models using three backbones. Though Le Vit-UNet-384 performs well, in our research work, LeVit-UNet-192 performed best.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128951205","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}

引用次数: 1

Polarization-Insensitive Terahertz Tunable Broadband Metamaterial Absorber on U-shaped Graphene Array u形石墨烯阵列上的偏振不敏感太赫兹可调谐宽带超材料吸收体

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101545

A. Hossain, Abdul Khaleque, N. Shahriar, Md. Sarwar Hosen, K. Shaha, M. Mizan

引用次数: 0

Protein Structure Prediction in Structural Genomics without Alignment Using Support Vector Machine with Fuzzy Logic 基于模糊支持向量机的无比对结构基因组学蛋白质结构预测

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10100743

Sharnali Saha, P. C. Shill

{"title":"Protein Structure Prediction in Structural Genomics without Alignment Using Support Vector Machine with Fuzzy Logic","authors":"Sharnali Saha, P. C. Shill","doi":"10.1109/ECCE57851.2023.10100743","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10100743","url":null,"abstract":"Protein secondary structure prediction from amino acid sequences is a challenging and complex task as it has become a must in oder to identifying the similarities/dissimilarities between protein structure. The protein secondary structure is used for studying the biological functionality of species in order to develop new drugs. A sustainable number of research has been done for predicting protein structure but yet the performance is not satisfactory. For this reason, it is necessary and time demanding to develop a technique for predicting protein structure that gives the satisfactory performance for large datasets termed as big datasets. In this article, propose a method based on the support vector machine and fuzzy logic in order to predict protein secondary structure without alignment. In this case, generate the optimal hyper plane of support vector machine using the membership values. Moreover, in order to increase the generalization ability a hybrid kernel support vector machine is propose that gives the better results in terms of classification and learning ability. We have tested the proposed method performance on the several benchmark datasets. The simulation results shows that the proposed technique outperforms better than other existing conventional techniques.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"49 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117118820","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}

引用次数: 0

Implementation of Liver Segmentation from Computed Tomography (CT) Images Using Deep Learning 利用深度学习实现计算机断层扫描(CT)图像的肝脏分割

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101544

MD Ashraf Hossain Ifty, Md. Salim Shahed Shajid

{"title":"Implementation of Liver Segmentation from Computed Tomography (CT) Images Using Deep Learning","authors":"MD Ashraf Hossain Ifty, Md. Salim Shahed Shajid","doi":"10.1109/ECCE57851.2023.10101544","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101544","url":null,"abstract":"Liver segmentation from computed tomography (CT) images has grown significantly in importance in the field of medical image processing in the last few years. It is the first and most crucial step in any computerized technique for the automatic detection of liver disease, liver volume measurement, and 3D liver volume rendering. The diagnosis and treatment of liver cancer depend heavily on the segmentation of the liver from CT images to get liver volumetric data, but manual segmentation is a strenuous and time-consuming process. The procedure can be accelerated, simplified, and made less error-prone by using deep learning methods. Image segmentation based on deep learning techniques has gained widespread acceptance due to its robustness, efficiency, and it's reproducible nature. Therefore, in this paper, using UNet, MONAI (Medical Open Network for Artificial Intelligence) and PyTorch framework, a deep-learning model to segment the liver from publicly available CT scan dataset was developed. The same ideas that underlie this model for segmenting the liver will allow to create models for segmenting other organs or malignancies using CT data. The goal is to develop a liver segmentation model that can quickly and accurately extract the liver from any given CT image with an accuracy that is on par of manual segmentation performed by a skilled radiologist.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121657780","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}

引用次数: 0

Detection and Recognition of Bangladeshi Vehicles' Nameplates Using YOLOV6 and BLPNET 基于YOLOV6和BLPNET的孟加拉车辆铭牌检测与识别

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101501

Camelia Sinthia, M. H. Kabir

引用次数: 1

RetNet: Retinal Disease Detection using Convolutional Neural Network RetNet:基于卷积神经网络的视网膜疾病检测

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101661

Amit Roy, Riasat Abdullah, Fahim Ahmed, Shahriar Mashfi, Sazid Hayat Khan, Dewan Ziaul Karim

{"title":"RetNet: Retinal Disease Detection using Convolutional Neural Network","authors":"Amit Roy, Riasat Abdullah, Fahim Ahmed, Shahriar Mashfi, Sazid Hayat Khan, Dewan Ziaul Karim","doi":"10.1109/ECCE57851.2023.10101661","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101661","url":null,"abstract":"Retina turns light into pictures and sends messages to the brain. A retinal disease might lead to vision loss or blindness due to eye illness, ocular trauma, or other disorders. Diabetic retinopathy, AMD, and retinal detachment are some widely known retinal-based illnesses. Having eye checkup once a year can assist in preserving the health of the retina. In this matter, the utilization of machine learning and computer vision can be of significant importance. This work proposes an inexpensive, quick approach to diagnose retinal diseases correctly. In today's environment, many people utilize cellphones and high-resolution cameras and hence using computer vision to detect retinal problems will help a lot. This work proposes a lightweight custom CNN model (RetNet) to accurately diagnose and classify retinal disorders. For extensive image recognition, the convolutional neural network was fed 30904 retinal images split into 3 categories: Test, train, and validation. Four retinal conditions: CNV, DME, DRUSEN and NORMAL were deteced and classified. The CNN model trained with these datasets achieved 97.85% training accuracy and 95.41% validation accuracy. Pre-trained models such as Resnet50, InceptionV3, EfficientNetB0, Xception, and VGG16 were also used and their accuracies were 79.34%, 91.32%, 28.0%, 87.94%, and 94.01% respectively. Based on the overall research, it was clear that our lightweight custom CNN model outperformed all pretrained models and produced superior accuracy than the previous works for the used dataset.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126196734","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}

引用次数: 2

Ensemble Machine Learning Approach For Agricultural Crop Selection 农业作物选择的集成机器学习方法

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101585

A. Islam, Imranul Khair, Sakawat Hossain, Rashedul Arefin Ifty, M. Arefin, M. Patwary

{"title":"Ensemble Machine Learning Approach For Agricultural Crop Selection","authors":"A. Islam, Imranul Khair, Sakawat Hossain, Rashedul Arefin Ifty, M. Arefin, M. Patwary","doi":"10.1109/ECCE57851.2023.10101585","DOIUrl":"https://doi.org/10.1109/ECCE57851.2023.10101585","url":null,"abstract":"The importance of agricultural earnings and employment in most countries has decreased with time. That is also true for Bangladesh. Farmers usually design the cultivation process based on their previous experience. Due to a lack of precise agricultural knowledge, they probably end up farming undesirable crops. Several research has employed machine learning methods to forecast agricultural output, but only a few used ensemble machine learning approaches. We use three major crop data which are Aus rice, Aman rice and Potato from the Bangladesh Bureau of Statistics and the seven weather parametrized data from the Bangladesh Meteorological Department over 43 years. The main contribution of this research is the development of an Ensemble Machine Learning Approach (EMLA) by using Catboost Regressor and XGBoost Regressor with their novel combination of Machine Learning Algorithms on the collected dataset. The study compares the accuracy and error rate of the proposed EMLA with eight well-known machine learning algorithms. Our proposed EMLA achieved a high degree of accuracy with R-squared scores of 88.084%,91.776% and 90% respectively for Aus rice, Aman rice and Potato. The results show that the EMLA technique improves the output and prediction by relying on the strong performance of another model. The primary goal of this research is to improve the predictability for overcoming food difficulties and create an intelligent information prediction analysis on farming in Bangladesh for efficient and profitable farming decisions. In this research, we proposed our Ensemble Machine Learning Approach for agricultural crop selection and yield prediction.","PeriodicalId":131537,"journal":{"name":"2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127390517","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}

引用次数: 0

Bandgap Analysis of InAs/InGaN Quantum Dot Intermediate Band Solar Cell (QDIBSC) InAs/InGaN量子点中间带太阳能电池(QDIBSC)的带隙分析

2023 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2023-02-23 DOI: 10.1109/ECCE57851.2023.10101673

Anik Das, Md. Mahmudur Rahman, M. A. Matin, N. Amin

引用次数: 0