MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis

• Published in: Research journal of pharmacy and technology Vol. 11; no. 2; pp. 434 - 438
• Main Authors: Devi, Shyamala M, Sruthi, A. N, Jothi, Saranya C
• Format: Journal Article
• Language: English
• Published: Raipur A&V Publications 2018
• Subjects: Algorithms; Automation; Classification; Clustering; Coding standards; Decomposition; Discriminant analysis; Image coding; Liver; Liver cancer; Neighborhoods; Neural networks; Noise; Pattern recognition; Signal processing; Tumors; Wavelet transforms; New York; United States > US
• ISSN: 0974-3618; 0974-360X; 0974-306X
• DOI: 10.5958/0974-360X.2018.00080.X

Contributions: In applications such as still image compression, discrete wavelets transform (DWT) based schemes have outperformed other coding schemes like the ones based on DCT. Since there is no need to divide the input image into non-overlapping 2-D blocks and its basis functions have variable length, wavelet-coding schemes at higher compression ratios avoid blocking artifacts. Because of their inherent multi -resolution nature, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important. Homogeneity: Measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal Homogeneity = sum (sum (p(x, y) / (1 + [x-y]))) 3.4.Supervised Classifier: Supervised learning with non knowledge based classifier will be used for gender classification. he neural network model PNN is used here to act as a classifier with radial basis function for network activation function. [...]test image features are simulating with trained network to make decision of gender Male or Female.