Autor/Autorin: Zeki Mohammed Esraa
B.Sc. in Computer Science, University of Mosul, Iraq, M.Sc. and Ph.D. in Computer Science, University of Sulaimani, Iraq. Research interests:Artificial Neural networks, CBIR, Database, Fractals, FPGA, Image Processing, Wireless Communication.
Breast cancer has become a major health problem over the last 50 years; it is a leading cause of cancer-related death in women population today. However, if breast abnormalities are detected and diagnoses are made at early stages, studies show that the chances of survival can be greatly improved. In this work, multi-scale fractal dimension is used to derive a set of textural features in order to perform texture analysis on breast tissues samples. The box counting method was used to estimate the multi fractal dimensions. The feed forward neural network is used to classify different types of breast tissues according to the extracted fractal dimension vectors.
Breast cancer has become a major health problem over the last 50 years; it is a leading cause of cancer-related death in women population today. However, if breast abnormalities are detected and diagnoses are made at early stages, studies show that the chances of survival can be greatly improved. In this work, multi-scale fractal dimension is used to derive a set of textural features in order to perform texture analysis on breast tissues samples. The box counting method was used to estimate the multi fractal dimensions. The feed forward neural network is used to classify different types of breast tissues according to the extracted fractal dimension vectors.
Über den Autor
B.Sc. in Computer Science, University of Mosul, Iraq, M.Sc. and Ph.D. in Computer Science, University of Sulaimani, Iraq. Research interests:Artificial Neural networks, CBIR, Database, Fractals, FPGA, Image Processing, Wireless Communication.
Klappentext
Breast cancer has become a major health problem over the last 50 years; it is a leading cause of cancer-related death in women population today. However, if breast abnormalities are detected and diagnoses are made at early stages, studies show that the chances of survival can be greatly improved. In this work, multi-scale fractal dimension is used to derive a set of textural features in order to perform texture analysis on breast tissues samples. The box counting method was used to estimate the multi fractal dimensions. The feed forward neural network is used to classify different types of breast tissues according to the extracted fractal dimension vectors.
