In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the...In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.展开更多
Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of th...Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of this disease has been demonstrated an approach to long survival of the patients. As an attempt to develop a reliable diagnosing method for breast cancer, we integrated support vector machine (SVM), k-nearest neighbor and probabilistic neural network into a complex machine learning approach to detect malignant breast tumour through a set of indicators consisting of age and ten cellular features of fine-needle aspiration of breast which were ranked according to signal-to-noise ratio to identify determinants distinguishing benign breast tumours from malignant ones. The method turned out to significantly improve the diagnosis, with a sensitivity of 94.04%, a specificity of 97.37%, and an overall accuracy up to 96.24% when SVM was adopted with the sigmoid kernel function under 5-fold cross validation. The results suggest that SVM is a promising methodology to be further developed into a practical adjunct implement to help discerning benign and malignant breast tumours and thus reduce the incidence of misdiagnosis.展开更多
基金Under the auspices of the Research Foundation of Doctoral Point of China(No.RFDP20010290006).
文摘In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.
基金Joint Research Project Between Chongqing University and National University of Singapore (No. ARF-151-000-014-112)the Basic Research & Applied Basic Research Program of Chongqing University (No.71341103)Natural Science Foundation of Chongqing S & T Committee(No. CSTC,2006BB5240)
文摘Diagnosis and treatment of breast cancer have been improved during the last decade; however, breast cancer is still a leading cause of death among women in the whole world. Early detection and accurate diagnosis of this disease has been demonstrated an approach to long survival of the patients. As an attempt to develop a reliable diagnosing method for breast cancer, we integrated support vector machine (SVM), k-nearest neighbor and probabilistic neural network into a complex machine learning approach to detect malignant breast tumour through a set of indicators consisting of age and ten cellular features of fine-needle aspiration of breast which were ranked according to signal-to-noise ratio to identify determinants distinguishing benign breast tumours from malignant ones. The method turned out to significantly improve the diagnosis, with a sensitivity of 94.04%, a specificity of 97.37%, and an overall accuracy up to 96.24% when SVM was adopted with the sigmoid kernel function under 5-fold cross validation. The results suggest that SVM is a promising methodology to be further developed into a practical adjunct implement to help discerning benign and malignant breast tumours and thus reduce the incidence of misdiagnosis.
