Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer
Algorithm 1
I. Initializing the iteration count variable,
II. Updating
III. Updating the weight as follows.
where and refer to the normal value set in advance, which we take as 0.01
IV. To judge whether to output or continue the next step according to the convergence condition/whether reaches the maximum cyclic value. If so, output ; if , proceed to the next step
Then, Equation (1) is solved by regarding and as constant values. Equation (1) is transformed according to the Lagrange function [21] as follows.
where represents the Grange multiplier and represents the compensation parameter.
To solve , we assume that other variables are fixed parameters; then,
Based on this, the equation below is obtained.
represents the singular value threshold operator of the matrix, expressed as follows.
represents the contraction operator of the matrix, .
To solve , we assume that other variables are fixed parameters; then,
Based on this, the equation below is obtained.
To solve , we assume that other variables are fixed parameters; then,
The Lagrange multiplier and parameter are used for optimization, and the specific update method is as follows.
