A computer-implemented method for classifying a digital image, the method comprising: obtaining, from a host computer, feature data corresponding to the digital image; determining, by a graphics processing unit, a semi-metric distance based on a Poisson-Binomial distribution between the feature data and one or more reference feature data stored in a memory of the host computer, wherein the semi-metric distance is a measure which does not satisfy the triangle inequality axiom; and classifying the digital image using the determined semi-metric distance, wherein the digital image comprises information corresponding to a DNA or RNA sequence, and the obtained feature data comprises a vector X of sequencing quality probabilities for a first DNA or RNA sample with a sequencing depth d x such that X = (x 1... x dx) and the reference feature data comprises a vector Y of sequencing quality probabilities for a reference DNA or RNA sample with a sequencing depth d y such that Y = y 1... y dy), each sequencing quality probability from the vector X indicating a probability of a correct read at a particular position in the first DNA or RNA sample, each sequencing quality probability from the vector Y indicating a probability of a correct read at a particular position in the reference DNA or RNA sample, and wherein the determining the semi-metric distance (PBR seq) comprises calculating: PBR seq X Y = σ X σ Y μ X − μ Y, wherein μx is a mean for vector X and is calculated by μ X = 1 d x ∑ i = 1 d x x i, μ Y is a mean for vector Y and is calculated by μ Y = 1 d y ∑ i = 1 d y y i, σ X is a standard deviation for vector X, and σ Y is a standard deviation for vector Y, wherein the classifying the digital image comprises: determining whether the semi-metric distance (PBR seq) is greater than a threshold value, and classifying the DNA or RNA sequence as being a tumor or normal based on the determining if the semi-metric distance (PBR seq) is greater than the threshold value. obtaining, from a host computer, feature data corresponding to the digital image; determining, by a graphics processing unit, a semi-metric distance based on a Poisson-Binomial distribution between the feature data and one or more reference feature data stored in a memory of the host computer, wherein the semi-metric distance is a measure which does not satisfy the triangle inequality axiom; and classifying the digital image using the determined semi-metric distance, wherein the digital image comprises information corresponding to a DNA or RNA sequence, and the obtained feature data comprises a vector X of sequencing quality probabilities for a first DNA or RNA sample with a sequencing depth d x such that X = (x 1... x dx) and the reference feature data comprises a vector Y of sequencing quality probabilities for a reference DNA or RNA sample with a sequencing depth d y such that Y = y 1... y dy), each sequencing quality probability from the vector X indicating a probability of a correct read at a particular position in the first DNA or RNA sample, each sequencing quality probability from the vector Y indicating a probability of a correct read at a particular position in the reference DNA or RNA sample, and wherein the determining the semi-metric distance (PBR seq) comprises calculating: PBR seq X Y = σ X σ Y μ X − μ Y, wherein μx is a mean for vector X and is calculated by μ X = 1 d x ∑ i = 1 d x x i, μ Y is a mean for vector Y and is calculated by μ Y = 1 d y ∑ i = 1 d y y i, σ X is a standard deviation for vector X, and σ Y is a standard deviation for vector Y, wherein the classifying the digital image comprises: determining whether the semi-metric distance (PBR seq) is greater than a threshold value, and classifying the DNA or RNA sequence as being a tumor or normal based on the determining if the semi-metric distance (PBR seq) is greater than the threshold value. determining whether the semi-metric distance (PBR seq) is greater than a threshold value, and classifying the DNA or RNA sequence as being a tumor or normal based on the determining if the semi-metric distance (PBR seq) is greater than the threshold value.