Combination machine learning algorithms for computer-aided detection, review and diagnosis

1. A method of reviewing medical images and clinical data to generate a diagnosis or treatment decision, comprising:

receiving, at a computer-aided detection (CAD) system, the medical images and clinical data;

processing, by the CAD system, the medical images and clinical data; to generate initial finding candidates;

clustering, by the CAD system, the initial finding candidates into a plurality of groups;

classifying, by the CAD system, the initial finding candidates into one or more categories using machine learning algorithms integrated into the CAD system and combined classifiers;

assessing, by the CAD system, one or more categories of the initial finding candidates using type 2 fuzzy logic;

determining, by the CAD system, detection and assessment statistics based on at least the assessed categories and classified findings using Bayesian probability analysis;

modifying, by the CAD system, the classified findings and assessed categories based on additional interactive input; and

generating the diagnosis or treatment decision based on the determined detection, assessment statistics, and the additional interactive input.

2. The method of claim 1 , wherein processing the medical images and clinical data comprises:

segmenting a region of interest from a background;

normalizing the region of interest;

filtering the normalized region of interest; and

generating the initial finding candidates from the normalized region of interest.

3. The method of claim 1 , wherein clustering the initial finding candidates into a plurality of groups comprises:

determining a number of clusters stored in a database;

measuring a distance between the clusters based on modality;

clustering the initial finding candidates using a hierarchical or partitional algorithm; and

saving a centroid from each initial finding candidate cluster.

4. The method of claim 3 , wherein clustering the initial finding candidates into a plurality of groups further comprises:

loading saved centroids;

calculating distances between at least one candidate of the initial finding candidates and each loaded centroid; and

classifying the at least one candidate of the initial finding candidates based on the calculated distance.

5. The method of claim 1 , wherein classifying the initial finding candidates comprises:

extracting selected features from the initial finding candidates;

performing, by the CAD system, a first classification using a first neural network trained by a first algorithm;

performing, by the CAD system, a second classification using a kernel-based method to map feature vectors of the extracted features into higher-dimensional space;

performing, by the CAD system, a third classification using a second neural network trained by a second algorithm;

combining the first, second, and third classifications; and

determining, by the CAD system, a minimum for each classification, a mean for each classification, and a maximum for each classification.

6. The method of claim 1 , wherein assessing one or more categories of the initial finding candidates using type 2 fuzzy logic comprises:

inputting linguistic variables into a fuzzifier to generate fuzzified results;

inputting the fuzzified results and additional rules into an interface;

reducing the fuzzified results by type;

defuzzifying the reduced results; and

translating the defuzzified results into formal language.

7. The method of claim 1 , wherein determining detection and assessment statistics based on at least the assessed categories and classified findings using Bayesian probability analysis comprises:

estimating parameters of a probability density function in a form of Rayleigh distribution using an expectation-maximization algorithm;

obtain prior probabilities; and

calculate posteriori probabilities of the assessed categories and classified results using the estimated parameters and prior probabilities.

8. The method of claim 1 , wherein the additional interactive input is provided by a human observer.