Volume-5 Issue-1, December 2024 – International Journal of Advanced Medical Sciences and Technology (IJAMST)

C3014061321

IJAMST Journal — Mon, 23 Dec 2024 11:19:39 +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

Skin Cancer Cell Detection Using Machine Learning and Image Processing
Devaraj Verma C¹, Prajwala R²

¹Dr. Devaraj Verma C, Professor, Department of Computer Science and Engineering, FET, Jain (Deemed to be University), Ramanagaram (D), Bangalore, (Karnataka), India.

²Prajwala R, Student, Department of Computer Science and Engineering, FET, Jain (Deemed to be University), Ramanagaram (D), Bangalore (Karnataka), India.

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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: Skin Cancer in today’s scenario is the most trending and common of all the cancers that directly affect the skin of the patient. It is the fifth most common type of cancer found in men and the sixth most common type in women. Surgery, Chemotherapy, Radiation therapy, and immunotherapy techniques are used to kill cancer cells. The research investigated cancer detection, thoroughly discussed it, and proposed the methodologies for early diagnosis of the diseases using image processing and machine learning. The proposed model is designed with the procedures of collection of dermoscopy images and undergoes pre-processing, segmentation, feature selection, and predictions. Multiple algorithms were utilized for the medical diagnosis of the cancer cells including Conventional neural network (CNN), Support vector machine (SVM), Decision Tree, and Random Forest to detect the cancer cells with higher accuracy.

Keywords: Skin cancer, Melanoma, CNN, SVM, Random forest.
Scope of the Article: Cancer Research

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C3008061321

IJAMST Journal — Mon, 23 Dec 2024 11:13:53 +0000

Electronic Health Records (EHR) Extraction Using Various NLP Models
Aman Mukhopadhyay

Aman Mukhopadhyay, Department of Computer Science and Engineering, VIT University, Vellore (Tamil Nadu), India.

Abstract: Information recorded in electronic medical health records, clinical reports, and summaries has the possibility of revolutionizing health-related research and its corresponding industry. EMR data can be used for epidemiological studies, disease registries, data banks, drug safety surveillance, clinical trials, and healthcare audits. With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems and to enable secondary use of EHRs for clinical and translational research; thereby increasing efficiency. One critical component which is predominantly used to facilitate the secondary use of EHR data is information extraction (IE) task, which automatically extracts and encodes clinical information from a given text. Now, a natural language processing model (NLP) focuses on “developing computational models for understanding the interaction between data science and language”. In the clinical domain, researchers have often used NLP systems to identify clinical syndromes and common biomedical concepts from imaging data, radiology reports, discharge summaries, problem lists, nursing documentation, drug reviews, and medical education documents. These data can help doctors determine patients’ health condition(s) including diagnostic information, procedures and tests performed, treatment results, drugs administered, and more. Therefore, we hope to gain some insights and develop strategies to improve the utilization of these NLP systems in the clinical domain. We hope to provide a vision for addressing the existing data challenge(s) in this domain. For this, we would look at the various models that have been used/published over the years and test them for their attributes including effectiveness, accuracy, precision, etc. We believe that adding a probabilistic graphical model framework for structured output prediction would further improve the performance of our system. This experiment remains our future work.

Keywords: Electronic Health Records, Machine Learning, Models, Natural Language Processing, Disease, Information Extraction, Processing Data.
Scope of the Article: Health Improvement Strategies

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Volume-5 Issue-1, December 2024 – International Journal of Advanced Medical Sciences and Technology (IJAMST)

C3014061321

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C3008061321

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