Machine Learning and Deep Learning Approaches for Anomaly Detection in Medical Image Analysis: Advancing Diagnostic Healthcare
Keywords:
Medical anomalies, Anomaly detection, Medical imaging modalities, machine learning, deep learningAbstract
Anomaly is an event or a behaviour that deviates from the normal, in this situation the problem arises that need to be solved. however, the underlying causes remains elusive. And it is very important to detect these anomalies. The definition of anomaly varies between domains. This paper addresses the medical anomaly which is a challenge in medical imaging modalities that need to be resolved for accurate disease diagnosis, Medical anomaly can be described as a structural or functional abnormality that is ambiguous. This paper describes various ML and DL approaches for anomaly detection for various diseases using imaging modalities and help researchers and medical practitioners to choose an appropriate technique for diagnosing the disease early with high accuracy.
Downloads
References
Shamitha S K, V Ilango(2018), “A survey on machine learning techniques for fraud detection in health care” ,International journal of engineering and technology.
M Siddiq (2020), “ML-BASED MEDICAL IMAGE ANALYSIS FOR ANOMALY DETECTION IN CT SCANS, X-RAYS, AND MRIS”,
B Nithya, V Ilango (2017), “Predictive analytics in healthcare using machine learning tools and techniques”, International conference on intelligent computing and control systems”, Doi:10.1109/ICCONS.2017.8250771
B Nithya, V Ilango(2019), “Evaluation of machine learning based optimized feature selection approaches and classification method for cervical cancer prediction”, springer nature applied science(2019) 1:61, https://doi.org/10.1007/s42452-019-0645-7
Ambrose E, Attley D. Jamie, Gould T (2006), BMJ publishing group
Husband J(2008), “Recommendation for cross sectional imaging in Cancer Management, Computer Tomography- CT: Magnetic Resonance Imaging -MRI: Positron Emissison Tomography-PET, Royal college of radiologists, ISBN 978-1-905034-13-0
H B Nandapuru , S S Salankar, V R Bora (2014) , “MRI brain cancer classification using support vector machine”,IEEE students conference of electrical,electronics and computer science(SCEECS)
Kiamia Rezari, Hamed Aghai( 2017), “Segmentation and classification of brain tumor CT images using SVM Kernel Width “ , Sicth international conference on information technology convergence and services, doi:10.5121/csit.2017.70304
Ojetunmibi T, Asagha P O, Okenwo U A(2023), “Pneumonia disease detection and classification system using naïve bayes”, Scientia Africana , vol 22, April 2023, pp 97-114
Daniel F polan, Samuel L brody, Robert A kaufman(2016),”Tissue segmentation of computer tomography images using random forest algorithm, a feasilbility study”, Doi: 10.1088/0031-9155/61/17/6553
Dr.Raghavendra K V(2022), “ Analyzing histopathological images by using machine learning techniques”, Doi: 10.1007/si3204-021-02217-4
Walita N, Hiroshi N, Kota A, Yoshiki K (2014), “Bone segmentation in CT liver images using K-means clustering for 3D ribcage surface modelling”, wseas transactions on biology and biomedicine
Nikitha Pai, V Ilango(2020), “LSTM Neural Network model for feature selection for financial series prediction”, Fourth International conference on I-SMAC, pp 672-677
Asghar A Shah, Hafiz A M Malik,Abdul H M, Abdullah A, Zaeem A B(2023),”Deep Learning Ensemble 2D CNN approach towards the detection of lung cancer”, scientific report 13, Article No: 2987(20203)
Wei li, Xuefeng lin, Xi chen(2020), “ Detecting alzheimers disease based on 4D fMRI: An exploration under deep learning framework”.
J M Ha, H M Sueng,Wonjae choi (2022), “ Autoencoder based detection of near surface defects in ultrasonic testing”, pubmed, PMID: 34798565, Doi: 10.1016/j.ultras.2021.10.6637
W Zhao, D Jiang, J P Queralta, T Westerlund (2020), “MSS U-Net: 3D segmentation of kidneys and tumors for Ct images with a multiscale supervised U-Net”, Informatics in Medicine Unlocked, vol 19,2020,100357
S Motamed, P Rogalla, F Khalvati(2021),” Data Augumentation using Generative Adversarial Networks(GANs) for GAN based detection of Pnemonia and covid -19 in chest X-rays”, Informatics in medicine unlocked, vol 27,2021,100779
S. Jayabharathi, V Ilango (2023),” Anomaly detection using machine learning techniques: A systematic review”, International conference on advances in data driven computing and intelligent systems”, Vol 698, springer, pp- 553-573.
Juan shan, Kaisar Alam S,Braina Garra, Yingatao Zang, Tahira Ahmed(2016), “Computer-aided disease diagnosis for breast ultrasound using computerized BI-RADS features and machine learning method”.Ultrasound Medical & Biology , https://doi.org/10.1016/j.ultrasmedbio.2015.11.016
M. Poongodi, Mounir hamdi,Mohit Malviya, Ashutosh sharma, Gaurav dhiman, Vimal S(2022), “Diagnosis and combating COVID-19 using variable oura smart ring with deep learning methods”, Personal and Ubiquitous Computing (2022) 26:25–35, https://doi.org/10.1007/s00779-021-01541-4
Hethav Modi, Jigna Hatahalia, Mohammad S, Rajesh Gupta, Sudeep Tanwar(2021),”Deep-learning based parkinsons disease classification using PET scan imaging data”, IEEE 6th International Conference on Computing, Communication and Automation (ICCCA),
Muntasir M, Siam Bin S, Md Salim Ahammad, Md Millon Uddin, Md Ishtyaq Muhammad, Asam Mohaimeneul Islam(2022), “Deep Learning based model for Alzheimer’s Disease detection using brain MRI images”, 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
L T Duong,Nhi H Le, Taan B Tran, Vuong M Ngo, P T Nguyen(2021),” Detection of Tubercolosis from chest X- ray images: Boosting performance with vision transformer and Transfer learning”, Expert systems with application.
Subrato Bharati, Prajoy podder, Rubaiyat Hossain Mondal M (2020),” Hybrid deep learning for detecting lung disease from X-ray images”, Journal informatics in medicine unlocked
Horry M J, Chakroborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N(2020), “ Covid-19 detection through transfer learning using multimodal imaging data” , IEEE Access 2020,8,149808-149824
Li Q, Cai W, Wang X, Zhou Y, Feng D D, Chen M ,(2014), ” Medical image classification with convolution neural network “ International conference of control automation robotics & vision, Singapore.
Ravi V, Narasimhan H, Chakroborthy C, Pham T D, (2021), “Deep learning based meta classifier approach for Covid -19 classification using CT scan and chest X-ray image classification”
Bharthy S, Pavithra B, Akshaya B(2022), “ Lung Cancer Detection Using Machine Learning”, International Conference on Applied Artificial Intelligence and Computing(ICCAAIC)
Sharmila Nageswaran,G Arunkumar, Anil kumar bisht,Shivlal mewada, J N V R Swarup Kumar, Malik Jawarneh, Evans Asenso(2022),”Lung cancer classification and prediction using machine learning and image processing”, pubmed central, PMID :36046454
Vinod Kumar, Chander prabha, Preeti sharma, Nitin Mittal (2024),” Unified deep learning model for enhaced lung cancer prediction with ResNet-50-101 and EfficientNEt -B3 using DICOM images”, BMC Medical Imaging 24, Article Number 63(2024).
N Kalaivani, N Manimaran, Dr S Sophia, D Devi D (2020),” Deep learning-based lung cancer detection and classification”, IOP conference series: Material science and Engineering.
Arslan K, Arif M,Amerah A, Bader F A, Farhan A, Hussain A, Gui sang choi(2023), “Breast cancer detection and prevention using machine learning”, PMID: 37835856, doi:10.3390/diagnostics13193113
Salman Zakhareya, Habib Izadkhah, Jaber karimpur(2023), “A new deep-learning-based model for breast cancer diagnosis from medical images”,diagnostics(2023), 13(11), 1944, https://doi.org/10.3390/diagnostics13111944
A P Malarvizhi,A Mofika, A Monapreetha, A M Arunagiri 2022), “Brain tumor classification using machine learning algorithm”, journal of physics, doi:10.1088/1742-6596/2318/1/012042
Akmalbek B A, Mukhriddin M, Taeg kuen W (2023), “Brain tumor detection based on deep learning approaches and magnetic resonance imaging”, PMID:37627200, doi:10.3390/cancers15164172
Esam O, Muhammad M, Habib D, Hatem A, Awais M, Mina I(2022), “Automatic detection of liver cancer using hybrid pre-trained model”, PMID: 35891111, doi:10.3390/s22145429
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
