Volume-1 Issue-4, August 2021 – International Journal of Advanced Medical Sciences and Technology (IJAMST)

C3022081321

IJAMST Journal — Mon, 16 Aug 2021 05:34:17 +0000

The International Journal of Advanced Medical Sciences and Technology (IJAMST) has ISSN 2582-7596 (online), an open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December. The journal aims to publish high-quality peer–reviewed original articles in the area of Medical Science that covers Anatomy, Biochemistry, Pharmacology, Pathology, Forensic Medicine, Cardiology, Dermatology & Venereology, Hematology, Anesthesia, Obstetrics & Gynecology, Pediatrics, Medicine, Physiology, Anatomy and Surgery and Dentary & Dental Surgery, Microbiology, Community Medicine, Ophthalmology, Otorhinolaryngology, Internal Medicine, General Surgery, Paediatrics, Orthopedics, Psychiatry, Radiology, Pulmonary Medicine, Dermatology and Venereal diseases, Infectious Diseases, Anesthesia, Cardiology, Diabetes, Cancer Research, Serology, Endocrinology, Urology, Neurosurgery, Geriatric Medicine, Gastroenterology, Neurology, Nephrology, Dentistry and Medical Education, Health Improvement Strategies. #Anatomy #Physiology #Biochemistry #Pharmacology #Pathology #Forensic Medicine #Cardiology #Dermatology & Venereology #Hematology #Anesthesia #Radiology #Obstetrics & Gynecology #Pediatrics #Medicine #Physiology #Anatomy and Surgery and Dentary & Dental Surgery #Microbiology #Community Medicine #Ophthalmology #Otorhinolaryngology #Internal Medicine #General Surgery #Paediatrics #Orthopedics #Psychiatry #Radiology #Pulmonary Medicine #Dermatology and Venereal Diseases #Infectious Diseases #Anesthesia, Cardiology #Diabetes, Cancer Research #Serology, Endocrinology #Urology, Neurosurgery #Geriatric Medicine #Gastroenterology #Neurology#Nephrology #Dentistry and Medical Education #Health Improvement Strategies #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

An IoT Based Health Monitoring System to Tackle COVID-19 in a Contagious Ward of Hospital
Sk. Shariful Alam¹, Md. Shakibul Islam², Md. Mustahsin Farhan Chowdhury³, Tanim Ahmed⁴

¹Sk. Shariful Alam, AstroCeNT@Nicolaus Copernicus Astronomical Centre, Warsaw, Poland.

²Md. Shakibul Islam, Student, Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Bangladesh.

³Md. Mustahsin Farhan Chowdhury, Pursuing, B.Sc. Degree, Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Bangladesh.

⁴Tanim Ahmed, Pursuing, B.Sc. Degree, Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Bangladesh.

Manuscript received on 27 July 2021 | Revised Manuscript received on 03 August 2021 | Manuscript Accepted on 15 August 2021 | Manuscript published on 30 August 2021 | PP: 11-18 | Volume-1 Issue-4 August 2021 | Retrieval Number:100.1/ijamst.C3022081321 | DOI: 10.54105/ijamst.C3022.081421

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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Regarding the highly contiguous novel coronavirus disease (COVID-19), it is unsafe for health professionals, being involved in a hospital isolation ward. The healthcare workers, stationed in a contagious ward provide relentless monitoring of specific health parameters of those patients. The rate of contagion proportionately depends on the time spent by the health workers in an isolation ward. This challenging task is fairly manageable by employing promising Internet-of-Things (IoT) based autonomous robots (i.e. smart bots) thus reducing the hazards of contamination to health workers. In this research paper, we introduce a smart bot that can periodically measure the health parameters of COVID-19 patients, for instance, body temperature, oxygen saturation levels, blood pressure, respiration rate, heart rate, blood glucose level, etc. The proposed smart bot will transfer from one patient to another in a structured pattern and autonomously collect, forward, store the data of the patients for further analysis. The goal of the work is to involve a significantly inferior number of health workers in contagious wards to control contagion, thus creating stress-free environs for health workers. Moreover, smart bots will offer health professionals to pay attention to non-COVID-19 patients and make things easier for regular health check-ups of individuals in need.
Keywords: Autonomous robot, Covid-19, RFID, RPPG.
Scope of the Article: Health Improvement Strategies

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C3018061321

IJAMST Journal — Fri, 06 Aug 2021 05:35:43 +0000

COVID-19 Data Analysis using Chest X-Ray
Ishtiaque Ahmed¹, Manan Darda², Neha Tikyani³, Rachit Agrawal⁴, Manjusha Joshi⁵

¹Ishtiaque Ahmed, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.

²Manan Darda, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.

³Neha Tikyani, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.

⁴Rachit Agrawal, Student, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.

⁵Dr. Manjusha Joshi, Assistant Professor, SVKM‘s Narsee Monjee Institute of Management Studies (NMIMS), Mumbai (Maharashtra), India.

Manuscript received on 02 July 2021 | Revised Manuscript received on 16 July 2021 | Manuscript Accepted on 15 August 2021 | Manuscript published on 30 August 2021 | PP: 5-10 | Volume-1 Issue-4 August 2021 | Retrieval Number:100.1/ijamst.C3018061321 | DOI:10.54105/ijamst.C3018.081421

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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep-learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID-19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

Keywords: COVID-19, CNN, Chest X-ray, ResNet18, Radiograph Images, Deep Learning
Scope of the Article: Chest X-ray

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C3016061321

IJAMST Journal — Fri, 06 Aug 2021 04:41:10 +0000

Brain Tumor Detection from MR Images using Image Process Techniques and Tools in Matlab Software
Faisal Rehman¹, Syed Sheeraz Shah², Hamadullah Panhwar³, Akhtar Hussain⁴, Sajjad Ali⁵, Shuaib Hussain⁶, Tarique Mehmood⁷, Saeed Rabbani⁸

¹Faisal Rehman, National University of Science of Technology Islamabad.

²Syed Sheeraz Ali, Sukkur IBA University Sindh Pakistan.

³Hamadullah Panhwar, Jilin University PR China.

⁴Dr. Akhtar Hussain Phul, Khairpur Medical College KMC Khairpur.

⁵Sajjad Ali Rajpar, Mohammad Ali Jinnah University Sindh Pakistan

⁶Shuaib Ahmed, Kunming University of Science and Technology, China.

⁷Saeed Rabbani, Sukkur IBA University.

⁸Tarique Mehmood, Quaid Awam University Sindh

Manuscript received on 10 July 2021 | Revised Manuscript received on 17 July 2021 | Manuscript Accepted on 15 August 2021 | Manuscript published on 30 August 2021 | PP: 1-4 | Volume-1 Issue-4, August 2021 | Retrieval Number: 100.1/ijamst.C3016061321 | DOI: 10.54105/ijamst.C3016.081421

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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.

Keywords: MRI-Simples, Classification, Brain Tumor, Detection, Image Process Technique and Tools.
Scope of the Article: Brain Tumor

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