Improving the Accuracy of Detecting Cancerous Tumors Based on Deep Learning on MRI Images
محورهای موضوعی :
Majlesi Journal of Telecommunication Devices
Milad Ghasemi
1
,
Maryam Bayati
2
1 - Sepahan Institute of Higher Education of Science and Techniques, Isfahan, Iran
2 - Sepahan Institute of Higher Education of Sciences and Techniques, Isfahan, Iran
تاریخ دریافت : 1401/06/19
تاریخ پذیرش : 1401/07/30
تاریخ انتشار : 1401/09/10
کلید واژه:
image processing,
Tumor Diagnosis,
medical images,
Neural network,
چکیده مقاله :
The continuous progress of photography technologies as well as the increase in the number of images and their applications requires the emergence of new algorithms with new and different capabilities. Among the various processes on medical images, the segmentation of medical images has a special place and has always been considered and investigated as one of the important issues in the processing of medical images. Based on this, in this research, a solution to diagnose the tumor through the use of a combined method based on watershed algorithm, co-occurrence matrix and neural networks has been presented, so that through the use of this combined solution, the tumor can be detected with high accuracy. Medical images diagnosed. According to the method used in this research, as well as the implementation of the solution in the Python environment and through the use of CV2 and SimpleITK modules, it is possible to set parameters such as accuracy, correctness, recall and Fscore criteria. which are always important parameters that are investigated in researches, improved compared to the past and achieved favorable results. This will increase the improvement of tumor detection in the brain compared to Thersholding and TKMeans methods.
منابع و مأخذ:
Kumar, D. M., Satyanarayana, D., & Prasad, M. G. (2021). “ MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier,” Journal of Ambient Intelligence and Humanized Computing, 12(2), 2867-2880.
Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M. ... & Vercauteren, T. (2018). “ Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE transactions on medical imaging,”37(7), 1562-1573.
Mohan, G., & Subashini, M. M. (2018). MRI based medical image analysis: “ Survey on brain tumor grade classification. Biomedical Signal Processing and Control, 39, 139-161.
Rajagopal, R. (2019). “ Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features,” International Journal of Imaging Systems and Technology, 29(3), 353-359.
Devkota, B., Alsadoon, A., Prasad, P. W. C., Singh, A. K., & Elchouemi, A. (2020). “ Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction,” Procedia Computer Science, 125, 115-123.
Bousselham, A., Bouattane, O., Youssfi, M., & Raihani, A. (2019). “Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area,” International journal of biomedical imaging, 2019.
Roy, S., Bhattacharyya, D., Bandyopadhyay, S. K., & Kim, T. H. (2017). “An iterative implementation of level set for precise segmentation of brain tissues and abnormality detection from MR images,”IETE Journal of Research, 63(6), 769-783.
Bahadure, N. B., Ray, A. K., & Thethi, H. P. (2018). “Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm,”Journal of digital imaging, 31(4), 477-489.
Abbas, W. A. (2020). “Genetic Algorithm-Based Anisotropic Diffusion Filter and Clustering Algorithms for Thyroid Tumor Detection,”Iraqi Journal of Science, 1016-1026.
Bahadure, N. B., Ray, A. K., & Thethi, H. P. (2017). “Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM,”International journal of biomedical imaging, 2017.
Yu, L. (2018). “Image noise preprocessing of interactive projection system based on switching filtering scheme,”Complexity, 2018.
Niaz, A., Memon, A. A., Rana, K., Joshi, A., Soomro, S., Kang, J. S., & Choi, K. N. (2020). “Inhomogeneous Image Segmentation Using Hybrid Active Contours Model With Application to Breast Tumor Detection,”IEEE Access, 8, 186851-186861.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., & Sánchez, C. I. (2017). “A survey on deep learning in medical image analysis. Medical image analysis,”42, 60-88.
Xu, M., Li, C., Zhang, S., & Le Callet, P. (2020). “State-of-the-art in 360 video/image processing: Perception, assessment and compression,”IEEE Journal of Selected Topics in Signal Processing, 14(1), 5-26.
Abdulrahman, A. A., Rasheed, M., & Shihab, S. (2021, May). “The Analytic of image processing smoothing spaces using wavelet,” In Journal of Physics: Conference Series (Vol. 1879, No. 2, p. 022118). IOP Publishing.
Jiao, L., & Zhao, J. (2019). “A survey on the new generation of deep learning in image processing,”IEEE Access, 7, 172231-172263.
Wu, T., & Yang, Z. (2021). “Animal tumor medical image analysis based on image processing techniques and embedded system,”Microprocessors and Microsystems, 81, 103671.
Bre, F., Gimenez, J. M., & Fachinotti, V. D. (2018). “Prediction of wind pressure coefficients on building surfaces using artificial neural networks,”Energy and Buildings, 158, 1429-1441.
Juang, C. F., Chen, G. C., Liang, C. W., & Lee, D. (2016). “Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations,”Applied Soft Computing, 46, 753-766.
Cheng, G., & Han, J. (2016). “A survey on object detection in optical remote sensing images,”ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.
Wang, X., Ning, C., & Xu, L. (2015). “Spatiotemporal saliency model for small moving object detection in infrared videos,” Infrared Physics & Technology, 69, 111-117.
Han, P., Du, J., Zhou, J., & Zhu, S. (2013). “An object detection method using wavelet optical flow and hybrid linear-nonlinear classifier,” Mathematical Problems in Engineering, 2013.
Zhu, C., He, Y., & Savvides, M. (2019). “Feature selective anchor-free module for single-shot object detection,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 840-849).
Antúnez, E., Marfil, R., Bandera, J. P., & Bandera, A. (2013). “Part-based object detection into a hierarchy of image segmentations combining color and topology,” Pattern Recognition Letters, 34(7), 744-753.
Vard, A., Jamshidi, K., & Movahhedinia, N. (2012). “Small object detection in cluttered image using a correlation based active contour model,” Pattern Recognition Letters, 33(5), 543-553.
Wang, L., Shi, J., Song, G., & Shen, I. F. (2017, November). “Object detection combining recognition and segmentation,” In Asian conference on computer vision (pp. 189-199). Springer, Berlin, Heidelberg.
Rahebi, J., Elmi, Z., & Shayan, K. (2018, June). “Digital image edge detection using an ant colony optimization based on genetic algorithm,” In 2010 IEEE Conference on Cybernetics and Intelligent Systems (pp. 145-149). IEEE.
Singh, K. K., Bajpai, M. K., & Pandey, R. K. (2015, March). “A novel approach for edge detection of low contrast satellite images,” International Society for Photogrammetry and Remote Sensing.
Chakraborty, N., Subudhi, P., & Mukhopadhyay, S. (2019). “Shock filter-based morphological scheme for texture enhancement,” IET Image Processing, 13(4), 653-662.
Drăguţ, L., Csillik, O., Eisank, C., & Tiede, D. (2014). “Automated parameterisation for multi-scale image segmentation on multiple layers,” ISPRS Journal of photogrammetry and Remote Sensing, 88, 119-127.
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2017, September). “Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge,”. In International MICCAI Brainlesion Workshop (pp. 287-297). Springer, Cham.
Varoquaux, G., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2017). “Assessing and tuning brain decoders: cross-validation, caveats, and guidelines,” NeuroImage, 145, 166-179.
Alam, M. S., Rahman, M. M., Hossain, M. A., Islam, M. K., Ahmed, K. M., Ahmed, K. T. ... & Miah, M. S. (2019). “Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm,” Big Data and Cognitive Computing, 3(2), 27.
Ejaz, K., Rahim, M. S. M., Rehman, A., Chaudhry, H., Saba, T., Ejaz, A., & Ej, C. F. (2018). “Segmentation method for pathological brain tumor and accurate detection using MRI,” International Journal of Advanced Computer Science and Applications, 9(8), 394-401.
Russ, J. C., & Russ, J. C. (2017). “Introduction to image processing and analysis,” CRC press.
