Medical Image Classification using Interesting Pruning and Machine Learning Algorithm
Keywords:
Medical imaging, ROI, Navie Bayes, neuroimaging, mining.Abstract
Accurate and timely identification of brain tumors using medical imaging is essential for effective treatment planning and patient outcomes. This paper presents a new technique for classifying brain tumors by integrating efficient pruning methods with the Naive Bayes (NB) algorithm. The procedure starts with the identification of regions of interest (ROIs) inside brain scans, aided by expertise in the field. Following that, an intricate pruning method is used to meticulously retain crucial attributes from these areas of interest (ROIs), so enhancing the Naive Bayes (NB) algorithm to achieve superior accuracy in classification. Various feature selection strategies are examined, especially tailored to the unique characteristics of brain tumor images, hence improving the algorithm's capacity to distinguish between various tumor kinds. The proposed methodology has undergone rigorous evaluation on many brain tumor datasets via empirical evaluations, demonstrating its efficacy. The technique has shown enhanced classification efficacy and comprehensibility. The integration of proficient pruning strategies with the Naive Bayes algorithm not only improves the advancement of brain tumor classification but also presents opportunities for efficient and resource-conserving clinical applications, serving as a crucial instrument for neuroimaging diagnostics. The suggested model is assessed using Python and achieves an accuracy of 98%.
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M. Biswas, V. Kuppili, L. Saba, D.R. Edla, H.S. Suri, A. Sharma, J.S. Suri, Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk, Med. Biol. Eng. Comput. 57 (2) (2019) 543–564, https://doi.org/10.1007/s11517-018-1897-x.
Yu J, Yao J, Zhang J, Yu Z, Tao D (2020) SPRNet: single-pixel reconstruction for one-stage instance segmentation. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.2969046
Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 57:101678
Ayadi W, Elhamzi W, Charfi I, Atri M (2019) A hybrid feature extraction approach for brain MRI classification based on Bag-of-words. Biomed Signal Process Control 48:144–152
Shang R, He J, Wang J, Xu K, Jiao L, Stolkin R (2020) Dense connection and depthwise separable convolution based CNN for polarimetric SAR image classification. Knowl. Based Syst 105542
Sushma M C, Bharathi D, “Brain Tumor Identification using Bilateral Filtering and Adaptive K-Means Clustering”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, No. 5, May 2016.
Raj C.P,S., Shreeja R. “Automatic brain tumor tissue detection in T-1 weighted MRI”, Proceedings of International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-4, Coimbatore, India, 17-18 March 2017.
Mathew R.A, Anto B.P., “Tumor Detection and Classification of MRI Brain Image Using Wavelet Transform And SVM”, Proceedings of the International Conference on Signal Processing and Communication, pp. 75-78, Coimbatore, India, 28-29 July 2017.
Gamage P.T, Ranathunga L., “Identification of Brain Tumor using Image Processing Techniques”, Independent Study, Final Report, Faculty of Information Technology University of Moratuwa, 2017.
Gupta M., Rao B.V.V.S.N.P., Rajagopalan V., “Brain Tumor Detection in conventional MR Images based on Statistical Texture and Morphological Features”, Proceedings of the International Conference on Information Technology, pp. 129-133, Bhubaneswar, India, 22-24 December 2016.
Singh G., Ansari M.A., “Efficient Detection of Brain Tumor from MRIs Using K-Means Segmentation and Normalizeds Histogram”, 1st India International Conference on Information Processing (IICIP), Delhi, India, pp. 1-6, 12-14 August 2016.
Sharma K., Gujral S., Kaur A., “Brain Tumor Detection based on Machine Learning Algorithms”, International Journal of Computer Applications, Vol. 103 – No.1, October 2014.
Akram M.U., Usman A., “Computer Aided System for Brain Tumor Detection and Segmentation”, Proceedings of the International Conference on Computer Networks and Information Technology, Abbottabad, Pakistan, pp. 299-302, 11-13 July 2011.
Hemanth G., Janardhan M., Sujihelen L., “Design and Implementing Brain Tumor Detection Using Machine Learning Approach”, Proceedings of the Third International Conference on Trends in Electronics and Informatics, Tirunelveli, India, pp. 1289-1294, 23-25 April 2019.
S. Chaplot, L.M. Patnaik and N.R. Jagannathan, “Classification of Magnetic Resonance Brain Images using Wavelets as Input to Support Vector Machine and Neural Network“, Biomedical Signal Processing and Control, vol. 1(1), 2006, pp. 86–92, doi: 10.1016/j.bspc.2006.05.002.
H. Selvaraj, S. Thamarai Selvi, D. Selvathi and L. Gewali, “Brain MRI Slices Classification using Least Ssquares Support Vector Machine”, International Journal of Intelligent Computing in Medical Sciences & Image Processing, vol. 1(1),2007, pp. 21-33, doi: 10.1080/1931308X.2007.10644134
M. Maitra and A. Chatterjee, “Hybrid Multiresolution Slantlet Transform and Fuzzy c-means Clustering Approach for Normal-Pathological Brain MR Image Segregation”, Medical Engineering & Physics, vol. 30(5), 2008, pp. 615–623, doi: 10.1016/j.medengphy.2007.06.009.
Grunauer and M. Vincze, “Using Dimension Reduction to Improve the Classification of High-dimensional Data”, OAGM Workshop, 2015.
M. Zhou, J. Scott, B. Chaudhury, L. Hall, D. Goldgof, K.W. Yeom, R. Gillies, Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches, Am. J. Neuroradiol. 39 (2) (2018) 208–216, https:// doi.org/10.3174/ajnr.A5391.
A. El-Baz, J.S. Suri (Eds.), Level Set Method in Medical Imaging Segmentation, CRC Press, 2019, https://doi.org/10.1201/b22435
M. Biswas, V. Kuppili, L. Saba, D.R. Edla, H.S. Suri, E. Cuadrado-Godia, J.S. Suri, State-of-the-art review on deep learning in medical imaging, Front. Biosci. 24 (2019) 392–426, https://doi.org/10.2741/4725.
Tandel GS, Balestrieri A, Jujaray T, Khanna NN, Saba L, Suri JS (2020) Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput Biol Med 122:103804
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