Abstract
Medical image is made of a pixel which shows a real-world object. In terms of understanding their relevance for insight, analysis and disease diagnosis, analysing medical image data techniques is difficult. Image categorization is a vital issue when performing image analysis tasks that is crucial to computer-aided diagnosis. To address the issue using methods and techniques available, we take advantage of the results of image processing, pattern identification as well as classification techniques and then confirming the image classification result using the expertise of medical experts. In addition to obtaining high accuracy, the primary concern of medical image classification is to ascertain which parts of the human body are affected by the disease. In this paper, we discussed a set of techniques involved in medical image classification. The primary goal of this paper is to compile the advancement done till now in medical image classification methods to increase the accuracy and sensitivity of the algorithm and how the classification algorithm evolves over a period of time.
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Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Zhang K (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131
Jiang Y, Li Z, Zhang L, Sun P (2007) An improved SVM classifier for medical image classification. In: International conference on rough sets and intelligent systems paradigms. Springer, Berlin, Heidelberg, pp 764–773
Kim HE, Cosa-Linan A, Santhanam N, Jannesari M, Maros ME, Ganslandt T (2022) Transfer learning for medical image classification a literature review. BMC Med Imaging 22(1):113
Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Advances in neural information processing systems 30
Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 2018 25th IEEE international conference on image processing (ICIP)
Gopal NN, Karnan M (2010) Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C means along with intelligent optimization techniques. In: 2010 IEEE international conference on computational intelligence and computing research. IEEE, pp 1–4
Patil RC, Bhalchandra AS (2012) Brain tumour extraction from MRI images using MATLAB. Int J Electron, Commun Soft Comput Sci Eng (IJECSCSE) 2(1):1
Dubey RB, Hanmandlu M, Vasikarla S (2011) Evaluation of three methods for MRI brain tumor segmentation. In: 2011 eighth international conference on information technology: new generations. IEEE, pp 494–499
Murthy TD, Sadashivappa G (2014) Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor. In: 2014 international conference on advances in electronics computers and communications. IEEE, pp 1–6
Geetha Ramani R, Balasubramanian L (2016) Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybern Biomed Eng 36(1):102–118
Rouhi R, Jafari M (2016) Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Syst Appl 46:45–59
Raj RJS, Shobana SJ, Pustokhina IV, Pustokhin DA, Gupta D, Shankar KJIA (2020) Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8:58006–58017
Garg G, Garg R (2021) Brain tumor detection and classification based on hybrid ensemble classifier. arXiv preprint arXiv:2101.00216
Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Progr Electromagnetics Res 137:1–17
Saritha M, Joseph KP, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy-based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156
Demidova LA (2021) Two-stage hybrid data classifiers based on SVM and kNN algorithms. Symmetry 13(4):615
Sivasangari A, Helen S, Deepa S (2022) Detection of abnormalities in brain using machine learning in medical image analysis. In: 2022 international conference on sustainable computing and data communication systems (ICSCDS). IEEE, pp 102–107
Jeyaraj PR, Samuel Nadar ER (2019) Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J Cancer Res Clin Oncol 145(4):829–837
Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Networks 10(5):1055–1064
Wang L (ed) (2005) Support vector machines: theory and applications, vol 177. Springer Science & Business Media
Chaplot S, Patnaik LM, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92
Camlica Z, Tizhoosh HR, Khalvati F (2015) Medical image classification via SVM using LBP features from saliency-based folded data. In: 2015 IEEE 14th international conference on machine learning and applications (ICMLA). IEEE, pp 128–132
Shahajad M, Gambhir D, Gandhi R (2021) Features extraction for classification of brain tumor MRI images using support vector machine. In: 2021 11th international conference on cloud computing, data science & engineering (Confluence). IEEE, pp 767–772
Othman MFB, Abdullah NB, Kamal NFB (2011) MRI brain classification using support vector machine. In: 2011 fourth international conference on modeling, simulation and applied optimization. IEEE, pp 1–4
Ramteke RJ, Monali KY (2012) Automatic medical image classification and abnormality detection using k-nearest neighbour. Int J Adv Comput Res 2(4):190
Mangai JA, Wagle S, Kumar VS (2013) An improved k nearest neighbor classifier using interestingness measures for medical image mining. Int J Biomed Biol Eng 7(9):550–554
Wagle S, Mangai JA, Kumar VS (2013) An improved medical image classification model using data mining techniques. In: 2013 7th IEEE GCC conference and exhibition (GCC). IEEE, pp 114–118
Rajendran P, Madheswaran M (2010) Hybrid medical image classification using association rule mining with decision tree algorithm. arXiv preprint arXiv:1001.3503
Satheesh KG, Raj ANJ (2017) Medical image segmentation and classification using MKFCM and hybrid classifiers. Int J Intell Eng Syst 10(6):9–19
Zhang YD, Wu L, Wang S (2011) Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Progr Electromagnetics Res 116:65–79
Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M (2014) Medical image classification with convolutional neural network. In: 2014 13th international conference on control automation robotics & vision (ICARCV). IEEE, pp 844–848
Yang W, Chen Y, Liu Y, Zhong L, Qin G, Lu Z, Feng Q, Chen W (2017) Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med Image Anal 35:421–433
Gupta M, Prasad SK, Rastogi D, Johri P (2021) Brain tumor classification using advanced computational techniques. In: 2021 3rd international conference on advances in computing, communication control and networking (ICAC3N). IEEE, pp 548–553
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
Menegola A, Fornaciali M, Pires R, Bittencourt FV, Avila S, Valle E (2017) Knowledge transfer for melanoma screening with deep learning. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE, pp 297–300
Hassan M, Ali S, Alquhayz H, Safdar K (2020) Developing intelligent medical image modality classification system using deep transfer learning and LDA. Sci Rep 10(1):1–14
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Bose, A., Garg, R. (2023). State-of-Art Review on Medical Image Classification Techniques. In: Murthy, B.K., Reddy, B.V.R., Hasteer, N., Van Belle, JP. (eds) Decision Intelligence. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-99-5997-6_4
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DOI: https://doi.org/10.1007/978-981-99-5997-6_4
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