Neural network design
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Introduction to Fuzzy Logic using MATLAB
Introduction to Fuzzy Logic using MATLAB
Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms
IEEE Transactions on Information Technology in Biomedicine
Backpropagation Algorithms for a Broad Class of Dynamic Networks
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
A novel method for pulmonary embolism detection in CTA images
Computer Methods and Programs in Biomedicine
Preparation of 2D sequences of corneal images for 3D model building
Computer Methods and Programs in Biomedicine
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Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer aided classification method in computed tomography (CT) images of lungs developed using artificial neural network. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The statistical parameters like mean, standard deviation, skewness, kurtosis, fifth central moment and sixth central moment are used for classification. The classification process is done by feed forward and feed forward back propagation neural networks. Compared to feed forward networks the feed forward back propagation network gives better classification. The parameter skewness gives the maximum classification accuracy. Among the already available thirteen training functions of back propagation neural network, the Traingdx function gives the maximum classification accuracy of 91.1%. Two new training functions are proposed in this paper. The results show that the proposed training function 1 gives an accuracy of 93.3%, specificity of 100% and sensitivity of 91.4% and a mean square error of 0.998. The proposed training function 2 gives a classification accuracy of 93.3% and minimum mean square error of 0.0942.