2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)最新文献

{"title":"Keynote Speakers","authors":"M. D. Myers","doi":"10.1109/icbsii51839.2021.9445157","DOIUrl":"https://doi.org/10.1109/icbsii51839.2021.9445157","url":null,"abstract":"The classical view of an information system is that it represents and reflects physical reality. We suggest this classical view is increasingly obsolete: digital technologies are now creating and shaping physical reality. We call this phenomenon the ontological reversal. The ontological reversal is where the digital version is created first, and the physical version second (if needed). This ontological reversal challenges us to think about the role of humans and technology in society. It also challenges us to think about our role as IS scholars in this digital world and what it means for our research agendas. This paper has recently been accepted for publication in MIS Quarterly. Professor. Dr. Wisnu Jatmiko Professor. Dr. Wisnu Jatmiko is a professor of Faculty of Computer Science, Universitas Indonesia, Indonesian Section Chair of The Institute of Electrical and Electronics Engineers (IEEE), and also Senior Member of IEEE. Topic: Development of Integrated Smart Portable TeleUltrasonography to Improve the Maternal and Infant Health during Pandemic Abstract: One indicator to improve children’s health is to reduce mortality rates in children, infants, and toddlers. That rate can be minimized by controlling conditions since mother’s womb regularly. Maternal and infant health monitoring is still limited because most of the doctors are concentrated in Java, specifically in the capital city, Jakarta. The 24% of Obgyn doctors, of which about 700 people operate in Jakarta. In Jayapura, only 12 Obgyn is available, which is only 0.3% of the total Obgyn in Indonesia. That condition is exacerbated by the COVID 19 pandemic where direct interaction between humans is also limited, including visits by pregnant women and children to hospitals. At the same time, the health monitoring process must continue during this pandemic. One indicator to improve children’s health is to reduce mortality rates in children, infants, and toddlers. That rate can be minimized by controlling conditions since mother’s womb regularly. Maternal and infant health monitoring is still limited because most of the doctors are concentrated in Java, specifically in the capital city, Jakarta. The 24% of Obgyn doctors, of which about 700 people operate in Jakarta. In Jayapura, only 12 Obgyn is available, which is only 0.3% of the total Obgyn in Indonesia. That condition is exacerbated by the COVID 19 pandemic where direct interaction between humans is also limited, including visits by pregnant women and children to hospitals. At the same time, the health monitoring process must continue during this pandemic.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124761139","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}

{"title":"A Multiclass Skin Lesion classification approach using Transfer learning based convolutional Neural Network","authors":"Cauvery K, P. Siddalingaswamy, Sameena Pathan, Noel D’souza","doi":"10.1109/ICBSII51839.2021.9445175","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445175","url":null,"abstract":"The rapid rise in skin diseases over the past decade has been a growing concern worldwide. Early detection, correct categorization, and accurate identification can result in the successful treatment of melanoma, thereby decreasing the morbidity and mortality rate. Thus, there is a significant need for a system that is capable of identifying skin diseases and precisely classifying them. The proposed work aims to develop a multi class classification system using transfer learning-based convolutional neural networks (CNN). In particular, the proposed solution classifies the dermoscopic images to 8 different categories namely Melanoma (MEL), Basal Cell Carcinoma (BCC), Actinic Keratosis (AK), Benign Keratosis (BKL), Dermatofibroma (DF), Vascular lesions (VASC) and Squamous Cell Carcinoma (SCC). Four state-of-art pre-trained models are used for this task. A functional model-based network is leveraged to embed these sub-models in a larger multi-headed neural network. This will allow the embedded model to be treated as a single large model. An ensemble approach, termed as blending, is employed to combine the predictions efficiently made by the sub-models. Additionally, a robust cropping strategy is implemented to deal with the uncropped images and their impact on the performance of the classifiers is investigated. The impact of applying blending technique to ensemble the pre-trained CNNs are investigated against the performance of the individual classifier. The proposed work is carried out on International Skin Imaging Collaboration (ISIC) 2019 dataset. In this work, the solution for task 1 of the challenge is presented and we obtained balanced multi-class accuracy of 81.2% on the dataset compiled from the original dataset.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121241004","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}

{"title":"Machine-Learning-Scheme to Detect Choroidal-Neovascularization in Retinal OCT Image","authors":"V. Rajinikanth, S. Kadry, R. Damaševičius, D. Taniar, Hafiz Tayyab Rauf","doi":"10.1109/ICBSII51839.2021.9445134","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445134","url":null,"abstract":"Eye is a fundamental sensory organ and any disease in eye will severely affect the sensory signal evaluation and conclusion making capability of the brain. The Choroidal-Neovascularization (CNV) is one of the harsh eye diseases in which a new blood-vessel grow from the choroid. Usually, the major cause of CNV is due to wet Age-Related-Macular-Degeneration (ARMD) and the formed new vessel will cause a leak in fluid which makes the retinal wet. The untreated CNV will lead to vision loss. In this research, detection of CNV using Optical-Coherence-Tomography (OCT) is presented using 484 images (242 Healthy and 242 CNV). In this work, a Machine-Learning-Scheme (MLS) is developed to examine the resized OCT of 256x256 pixels and the stages of this MLS includes; pre-processing, feature extraction, Mayfly-Optimization-Algorithm (MFA) based feature reduction, and two-class classification. The experimental outcome of this technique confirmed that the Fine-Gaussian-SVM (SVM-FG) classifier helped to accomplish an improved classification accuracy (>92%) compared to the alternative classifiers of this study.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128019671","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}

{"title":"DCGAN based Pre-trained model for Image Reconstruction using ImageNet","authors":"Nandini Kumari, Shamama Anwar, Vandana Bhattacharjee","doi":"10.1109/ICBSII51839.2021.9445128","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445128","url":null,"abstract":"Despite recent achievements in generative image modeling, generating better quality image samples from complex datasets such as ImageNet remains an illusory goal. The objective of this paper is to train Deep Convolutional Generative Adversarial Network at the well-known CIFAR10 dataset and study the instabilities specific to such scale and then test the large-scale ImageNet dataset for establishment of the proposed DCGAN. We find that applying a pre-trained DCGAN can remove the complexity and also can learn prior details of images and improve the quality of generated image. Our modifications on DCGAN lead to models which set the new cutting edge in class-contingent image reconstruction on pre-trained GAN's. When tested on ImageNet at 128 × 128 resolution, our model (DCGAN) lowers the loss between the generated and real image samples which shows that the proposed DCGAN model works well with both the datasets.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114483701","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}

{"title":"A Comparative Study of Various Multimodal Medical Image Fusion Techniques– A Review","authors":"Kanike Vijay Kumar, A. Sathish","doi":"10.1109/ICBSII51839.2021.9445149","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445149","url":null,"abstract":"The main objective of image fusion for multimodal medical images is to retrieve valuable information by combining multiple images obtained from various sources into a single image suitable for better diagnosis. In this paper, a detailed survey on various existing medical image fusion algorithms, with a comparative discussion is presented. Image fusion algorithms available in the current literature are categorized into various methods known as (1) morphological methods, (2) human value system operator based methods, (3) sub-band decomposition methods, (4) neural network based methods, and (5) fuzzy logic based methods. This research concludes that even though there exists a few open-ended creative and logical difficulties, the fusion of medical images in many combinations assists in utilizing medical image fusion for medicinal diagnostics and examination. There is tremendous progress in the fields of deep learning, artificial intelligence and bio-inspired optimization techniques. Effective utilization of these techniques can be used to further improve the efficiency of image fusion algorithms.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"643 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116470396","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}

{"title":"TSSummarize: A Visual Strategy to Summarize Biosignals","authors":"João Rodrigues, Phillip Probst, H. Gamboa","doi":"10.1109/ICBSII51839.2021.9445154","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445154","url":null,"abstract":"Visual tools enhance the human ability to detect structures found on time series. Medical doctors and data-scientists rely on their visual abilities to perform time series analysis. A visual tool that would summarize several sources of information of time series would be of great value and is not yet provided in the literature. This work proposes a novel unsupervised visual strategy to summarize a time series and compact several layers of information. The strategy extracts information from the Self-Similarity Matrix (SSM). This data source is able to segment the time series, detect events and show relationships between subsequences. The visual strategy has been tested on several use-cases from the medical domain, proving to be type agnostic, intuitive and compact.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130547710","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}

{"title":"Automatic Anesthesia Control System","authors":"Shelishiyah Raymond, Susmitha Edagottu, Lasngewhun Mawblei, M. Ahmed","doi":"10.1109/ICBSII51839.2021.9445168","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445168","url":null,"abstract":"Providing painless surgery and delivering an accurate dose of anesthesia to the patient plays a very crucial role in any major surgeries. Failing in providing an accurate dose to the patient may show adverse effects and postoperative complications. In case of major surgeries which could take a longer period, the complete dosage of anesthesia could not be administered in a single dose to the patient since it may show lethal complications. Administering less dose of anesthesia makes the patient regain consciousness during the surgery. It is not easy for the anesthetist to deliver an accurate dose of anesthesia at regular intervals of time. To overcome such complications during surgeries a computer-controlled syringe is designed to deliver an accurate dose of anesthesia at regular intervals of time with constant speed. Therefore this project aims to introduce an automatic anesthesia control system integrating with monitoring parameters using Arduino Uno. This Embedded system uses a syringe pump to deliver the right amount of anesthesia to the patient. The anesthetist can set the desired amount of anesthesia that can be given to the patient with the help of a switch panel. Once the Arduino Uno receives the signal it activates the motor driver to drive the syringe pump at the preset intervals. The anesthesia is delivered to the patient according to the rotation of the stepper motor. After administration of anesthesia, the vital parameters like Temperature, Exhalation breath temperature, and Pulse are monitored side by side. If they are under the normal state then the second dose of anesthesia will be injected. On the onset of abnormality the doctor will be notified through a buzzer and anesthesia delivery would continue only if everything is under normal. Additionally, these parameters are checked by corresponding sensors. This integration of monitoring parameters increases the patient’s safety and keeps the anesthesiologists at ease.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121295233","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}

{"title":"Automated EEG Analysis for Early Diagnosis of Epilepsy: A Comparative Study to Determine Relative Accuracy of Arithmetic and Huffman Coding Algorithms","authors":"Anisha Kumar, Pratishtha Singh, Rajlakshmi Khawas, Priscilla Dinkar Moyya, Mythili Asaithambi","doi":"10.1109/ICBSII51839.2021.9445169","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445169","url":null,"abstract":"Epilepsy is a prevalent neurological disorder typically characterized by recurrent seizure activity and detected using an electroencephalogram (EEG). The manual inspection of EEG however is a challenging and slow process that is susceptive to visual errors and variability amongst subjects. Hence, significant efforts have been made towards developing algorithms for automated epilepsy diagnosis and detection. The present study focuses on comparing two algorithms employing arithmetic encoding and Huffman encoding to separate epileptic signals from seizure-free (normal) samples. The proposed diagnostic technique comprises three major steps. In the first step, discrete wavelet transform (DWT) is used to decompose the EEG signal into detail and approximation coefficients. The second step involves computation of compression ratios using encoding techniques to convert the significant coefficients into bitstreams. Finally, the compression vector set is normalized and fed to a machine learning classifier that identifies seizure activity from normal, seizure free signals. The study utilizes the standard database for epilepsy as provided by the University of Bonn in order to validate the results against prior benchmarks. The proposed methodology with arithmetic encoding algorithm achieved 100% accuracy and the classification results vary from 30.6% to 100% respectively in case of Huffman encoding. Hence, a computer aided diagnostic (CAD) technique employing DWT along with arithmetic encoding and machine learning algorithms would form a robust diagnostic system in early-stage epilepsy diagnosis.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762120","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}

{"title":"Design and Development of Non-invasive Automated mucus Removal Device By Acoustic Assisted Therapy","authors":"K. A. Bhavani, L. Sujitha","doi":"10.1109/ICBSII51839.2021.9445192","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445192","url":null,"abstract":"Mucus is a complex biological material that lubricate and protects the human lung and serves as a physical barrier against foreign particles. If there is a high mucus secretion, it blocks the airway and causes breathing difficulties that leads to many respiratory problems like cystic fibrosis, asthma and Chronic obstructive pulmonary disease (COPD). At present, an invasive device namely, suction catheter is used for removing excess secretion in bedside patients not for outpatients, and it causes more discomfort to the patients. There are no available devices to diagnose and remove the excess mucus secretion from the lungs at an early stage. Physiotherapists perform manual Chest physiotherapy with bronchial drainage method for treating respiratory problems, which is a standard treatment for mobilization and removal of airway secretion. But they are facing difficulties in removing secretion through this airway clearance technique and have found no device to analyze the presence of mucus. In order to overcome these problems, we are proposing a non-invasive auto mucus removal device by acoustic assisted therapy which will help doctors for diagnosing and removing excess lung secretions in both adult and pediatricians.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121790546","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}

{"title":"Design of a Medical Prototype Robot for Nurse Assistance","authors":"Steeve Shibu Chempolil, Renie Melvinia Basaiawmoit, Sneha Saji, Karthik Raj V","doi":"10.1109/ICBSII51839.2021.9445165","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445165","url":null,"abstract":"Robotics in the medical field is an ongoing trend in both research and commercial sectors. Robots are used in every hospital department for assistance in delivering things, surgery, checking vital signs, telepresence, etc. Medical prototype robot is a scenario-based assistive robot with a customized design to help the hospital staff fight against Covid-19 (Coronavirus disease) outbreak, ensuring social distancing. It has basic features like delivering medicine and small handheld devices, remote temperature sensing using IR (infrared) and UVC (ultraviolet type C) disinfection unit. The main aim of this prototype is to make the nurse not to handle the devices which was handled by the patients in which we can convey the information through an audio system (which is already available in the hospital) or a nurse will be assisting the initial instructions required (by ensuring the social distance) that is in the isolated ward so that the patient can do the task properly. For this prototype, we are using the basic microcontroller, that is, Arduino UNO. We were successful in taking readings with the help of a temperature sensor and were able to supply power to the UVC lamp in which it sterilized the objects inside the unit when it was exposed for 2-3 minutes. And finally, the robot was able to move successfully with the help of Arduino and Bluetooth setup.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114454589","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}