Department of Computer Engineering, Taiyuan Institute of Technology, Taiyuan 030008, Shanxi, China
The development of medical images has facilitated the diagnosis of brain diseases. The diagnosis of brain medical images has the characteristics of uneven distribution of categories and different costs of misclassification. Therefore, traditional classification algorithms are used in clinically confirmed MRI brains. When a medical image is used as a training set to construct a classification model, the classification effect is poor and it is easy to be insensitive to the positive class, which makes it difficult for the brain disease auxiliary diagnosis system to have high accuracy and weak generalization ability. The research purpose of this paper is to study the assistant diagnosis system of brain diseases based on the uneven distribution of medical image categories. In order to improve the performance of the assistant diagnosis system of brain diseases, this paper designs a cost-sensitive probabilistic neural network CS-PNN brain by introducing cost-sensitive this system is an auxiliary diagnosis system for diseases, and the reliability of the system is verified by experiments. It can be known from experiments that the cost-sensitive probabilistic neural network CS-PNN assisted diagnosis system for brain diseases designed in this paper increases with the cost of positive misclassifications and negative misclassifications, and the classification accuracy rate of CS-PNN continues to increase. (01) = 4 achieves the best classification performance of 97%. The research in this article provides new ideas for solving the problems of uneven distribution of categories and misclassification costs in MRI brain medical images, so as to develop a brain disease auxiliary diagnosis system with stronger generalization ability, thereby improving the diagnosis of brain tumors. Accuracy and reduce missed diagnosis.
Medical Images, Imbalanced Distribution of Categories, Brain Diseases, Assisted Diagnosis Systems, Cost Sensitive
Junxiu Wang. Assisted Diagnosis System for Brain Diseases with Imbalanced Category Distribution Based on Medical Images. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 6: 53-66. https://doi.org/10.25236/IJFET.2021.030606.
 Teitelbaum, Gillian A, Teitelbaum, Jonathan E. Uneven Distribution of Microgranules in Divided Lansoprazole Tablets [J]. Journal of Pediatric Gastroenterology & Nutrition, 2015, 61(4):437-9.
 Yipeng Liu, Chaoqing Xu, Zhechen Jiang. A Survey on Brain Fiber Visualization [J]. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2018, 30(1):9.
 Yang, Haiwei, Wang, Fei, Jiang, Peilin. Accurate Anatomical Landmark Detection Based on Importance Sampling for Infant Brain MR Images [J]. Journal of Medical Imaging & Health Informatics, 2017, 7(5):1078-1086.
 Zhang, Xiyu, Liu, Xinqi, Lin, Weiwei. Computing Optimization Technique in Enhancing Magnetic Resonance Imaging and Brain Segmentation of Hypophysis Cerebri Based on Morphological Based Image Processing [J]. Journal of Medical Imaging & Health Informatics, 2016, 6(4):1063-1070.
 G Kagadis, C Alexakos, P Papadimitroulas. SU-E-I-69: A Cloud Based Application for MRI Brain Image Processing [J]. Medical Physics, 2015, 42(6):3257.
 Biswajit Biswas, Biplab Kanti Sen. Color PET-MRI Medical Image Fusion Combining Matching Regional Spectrum in Shearlet Domain[J]. International Journal of Image and Graphics, 2019, 19(1):1950004.
 Snehashis Roy, Qing He, Elizabeth Sweeney. Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation [J]. IEEE Journal of Biomedical & Health Informatics, 2015, 19(5):1598.
 Subhranil Koley, Ashwini Galande, Bhooshan Kelkar. Multispectral MRI Image Fusion for Enhanced Visualization of Meningioma Brain Tumors and Edema Using Contourlet Transform and Fuzzy Statistics: [J]. Journal of Medical & Biological Engineering, 2016, 36(4):1-15.
 Mutasem K. Alsmadi. MRI Brain Segmentation Using a Hybrid Artificial Bee Colony Algorithm with Fuzzy-C Mean Algorithm [J]. Journal of Applied Sciences, 2015, 15(1):100-109.
 Yuanpeng Zhang, Li Wang, Huiqun Wu. A Clustering Method Based on Fast Exemplar Finding and Its Application on Brain Magnetic Resonance Images Segmentation [J]. Journal of Medical Imaging & Health Informatics, 2016, 6(5):1337-1344.
 Pan, Xiang, Liu, Shuangshuang, Jiang, Taiping. Non-Causal Fractional Low-Pass Filter Based Medical Image Denoising[J]. Journal of Medical Imaging & Health Informatics, 2016, 6(7):1799-1806.
 Hilal Naimi, Amel Baha Houda Adamou-Mitiche, Lahcène Mitiche. Medical image denoising using Dual Tree Complex Thresholding Wavelet Transform and Wiener filter [J]. Journal of King Saud University - Computer and Information Sciences, 2015, 27(1):40-45.
 Mostafa Heydari, Mohammad Reza Karami. A New Adaptive Diffusive Function for Magnetic Resonance Imaging Denoising Based on Pixel Similarity [J]. Journal of Medical Signals & Sensors, 2015, 5(4):201-209.
 Shuo Yang, Jianxun Li. The Design of Composite Adaptive Morphological Filter and Applications to Rician Noise Reduction in MR Images [J]. International Journal of Imaging Systems & Technology, 2015, 25(1):15-23.
 Wei Zhao, Tianye Niu, Lei Xing. MO-FG-204-03: Using Edge-Preserving Algorithm for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT [J]. Medical Physics, 2015, 42(6):3569.
 Dangguo Shao, Ting Zhou, Fan Liu. Ultrasound speckle reduction based on fractional order differentiation [J]. Journal of Medical Ultrasonics, 2016, 44(3):1-11.
 Daniel Haak, Charles-E Page, Thomas M Deserno. A Survey of DICOM Viewer Software to Integrate Clinical Research and Medical Imaging [J]. Journal of Digital Imaging, 2015, 29(2):1-10.
 Felix Fischer, M. Alper Selver, Sinem Gezer. Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data [J]. Journal of Medical & Biological Engineering, 2015, 35(6):709-723.
 T Kimpe, J Rostang, G Van Hoey. WE-D-204-04: Color Standard Display Function (CSDF): A Proposed Extension of DICOM GSDF [J]. Medical Physics, 2015, 42(6):3670.
 Bing-jin Liang, Yue-yin Zhang, Yan-jun Lin. Study on the technology of medical image reading based on JPEG2000 [J]. Automatic Control & Computer Sciences, 2016, 50(4):278-284.
 Padmapriya Praveenkumar, P. Catherine Priya, J. Avila. Tamper Proofing Identification and Authenticated DICOM Image Transmission Using Wireless Channels and CR Network [J]. Wireless Personal Communications, 2017, 97(4):5573-5595.
 Reiji KATAYAMA. Series: Practical Evaluation of Clinical Image Quality (3): Subjective Evaluation of Image Quality in Digital Radiography Systems [J]. Igaku Butsuri, 2016, 36(2):113-T
 Moser, S Stefanowicz, B Rhein. SU-F-J-21: Clinical Evaluation of Surface Scanning Systems in Different Treatment Locations [J]. Medical Physics, 2016, 43(6):3410-3410.120.
 M Schmidt, N Knutson, J Herrington. SU-F-T-262: Commissioning Varian Portal Dosimetry for EPID-Based Patient Specific QA in a Non-Aria Environment [J]. Medical Physics, 2016, 43(6):3522-3523.
 Y Altundal, D Pokhrel, H Jiang. SU‐F‐I‐02: Comparative Analysis and Constancy Check of Image Quality Parameters for Three Linear Accelerators Per TG 142 Protocol [J]. Medical Physics, 2016, 43(6):3386-3387.