MedicHub – Disease Detection Using Deep Learning
DOI:
https://doi.org/10.12723/mjs.sp2.3Keywords:
CConvolutional Neural Network, Real-Time Detection, Magnetic Resonance Imaging, Natural Language ProcessingAbstract
The integration of technology in healthcare is rapidly revolutionizing the sector and transforming the traditional modus operandi that used to be followed into a more efficient and accurate automated system. Machine Learning is a sophisticated technology used to analyze clinical symptoms to predict diseases and deliver accurate diagnoses based on strong evidence. The major advantage of using technology to assist in diagnosis is to understand more about
underlying illnesses that are often overlooked while searching for a more severe disease, or when the patient is not in imminent danger. This offers patients a very reliable and accessible alternative for immediate results and also minimizes the risk of errors. Another extremely good utility of technology is withinside the discipline of medical image analysis. CNN are neural networks which are capable of recognizing patterns in pictures and hence must be included in the system to increase its accuracy and efficacy.
References
Khedikar, Shipra, and Uma Yadav. "Identification of Disease by Using SVM Classifier." International Journal of Advanced Research in Computer Science and Software Engineering 7.04 (2017).
Mathew, A & Anto, Babu. (2017). Tumor detection and classification of MRI brain image using wavelet transform and SVM. 75-78. 10.1109/CSPC.2017.8305810.
Qodri, Krisna Nuresa, Indah Soesanti, and Hanung Adi Nugroho. "Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning." IJITEE (International Journal of Information Technology and Electrical Engineering) 5.1: 21-28.
Othman, Mohd Fauzi, and Mohd Ariffanan Mohd Basri. "Probabilistic neural network for brain tumor classification." 2011 Second International Conference on Intelligent Systems, Modelling and Simulation. IEEE, 2011.
Parajuli, Anil. DISEASE PREDICTOR. Diss. Ph. D. diss., Tribhuvan University, 2016.
Kareem, Shahab & Abdulrahman, Bikhtiyar & Hawezi, Roojwan & Khoshaba, Farah & Askar, Shavan & Muheden, Karwan & Abdulkhaleq, Ibrahim. (2023). Comparative evaluation for detection of brain tumor using machine learning algorithms. 10.11591/ijai.v12.i1.pp469-477.
K. M. Al-Aidaroos, A. A. Bakar and Z. Othman, "Naïve bayes variants in classification learning," 2010 International Conference on Information Retrieval & Knowledge Management (CAMP), 2010, pp. 276-281, doi: 10.1109/INFRKM.2010.5466902.
Goudouris ES. Laboratory diagnosis of COVID-19. J Pediatr (Rio J). 2021;97:7–12.
Chandra, Saroj Kumar and Manish Kumar Bajpai. “EFFECTIVE ALGORITHM FOR BENIGN BRAIN TUMOR DETECTION USING FRACTIONAL CALCULUS.” TENCON 2018 - 2018 IEEE Region 10 Conference (2018): 2408-2413.
Gurbina, Mircea et al. “Tumor Detection and Classification of MRI Brain Image using Different Wavelet Transforms and Support Vector Machines.” 2019 42nd International Conference on Telecommunications and Signal Processing (TSP) (2019): 505-508.
M Vamshi Krishna Reddy, G V P Sai Abhijith, K Sai Nath, Mangali Sathyanarayana."Disease Predictor Based on Symptoms Using Machine Learning", Volume 10, Issue VI, International Journal for Research in Applied Science and Engineering Technology (IJRASET) Page No: 2549-2555, ISSN : 2321-9653, doi - 10.22214/ijraset.2022.44408
Surendra Patro et al 2022 IOP Conf. Ser.: Earth Environ. Sci. 1032 012045, doi - 10.1088/1755-1315/1032/1/012045
Yan Y, Yao X-J, Wang S-H, Zhang Y-D. A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network. Biology. 2021; 10(11):1084. https://doi.org/10.3390/biology10111084
S. Ghulyani, D. Jain, P. Singh, S. Joshi and A. Ahlawat, "A Data Entry Optical Character Recognition Tool using Convolutional Neural Networks," 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET), Arad, Romania, 2022, pp. 721-728, doi: 10.1109/GlobConET53749.2022.9872395.
Kaveh Nasiri, Aleksandra Dimitrova, Comparing saliva and nasopharyngeal swab specimens in the detection of COVID-19: A systematic review and meta-analysis, Journal of Dental Sciences, Volume 16, Issue 3, 2021, Pages 799-805, ISSN 1991-7902, https://doi.org/10.1016/j.jds.2021.01.010.
A. Castiglione, M. Umer, S. Sadiq, M. S. Obaidat and P. Vijayakumar, "The Role of Internet of Things to Control the Outbreak of COVID-19 Pandemic," in IEEE Internet of Things Journal, vol. 8, no. 21, pp. 16072-16082, 1 Nov.1, 2021, doi: 10.1109/JIOT.2021.3070306.
P. Ouppaphan, "Corn Disease Identification from Leaf Images Using Convolutional Neural Networks," 2017 21st International Computer Science and Engineering Conference (ICSEC), Bangkok, Thailand, 2017, pp. 1-5, doi: 10.1109/ICSEC.2017.8443919.
Amirhossein Ahmadieh-Yazdi, Ali Mahdavinezhad, Leili Tapak et al. Identifying metastatic biomarkers of colorectal cancer: Machine learning modeling and experimental validation, 13 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2618089/v1]
S. K. Chandra and M. Kumar Bajpai, "EFFECTIVE ALGORITHM FOR BENIGN BRAIN TUMOR DETECTION USING FRACTIONAL CALCULUS," TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South), 2018, pp. 2408-2413, doi: 10.1109/TENCON.2018.8650163.
A. Gokarn, K. Patni, Y. Purohit and R. Sonkusare, "COVID-19 Radiography Using ConvNets," 2022 4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, Russian Federation, 2022, pp. 407-411, doi: 10.1109/SUMMA57301.2022.9973882.
K.M. Al-Aidaroos, A.A. Bakar and Z. Othman, 2012. Medical Data Classification with Naive Bayes Approach. Information Technology Journal, 11: 1166-1174. DOI: 10.3923/itj.2012.1166.1174
Irshad Ullah, “Data Mining Algorithms And Medical Sciences”,IJCSIT, Vol 2, No 6, December 2010
Amit Kumar Das, Aman Kedia,“Data mining techniques in Indian Healthcare: A Short Review”, 2015 International Conference on Man and Machine Interfacing (MAMI), 978-1-5090-0225-2/15
M. Saranya, N. Archana, J. Reshma, S. Sangeetha and M. Varalakshmi, "OBJECT DETECTION AND LANE CHANGING FOR SELF DRIVING CAR USING CNN," 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 2022, pp. 1-7, doi: 10.1109/IC3IOT53935.2022.9767882.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Nilesh Patil, Aaditya Gadiyar, Darshan Mehta, Harsh Khatri
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
