Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen

Algorithm 1

PS-ACO-RNN cancer detection mechanism.

	Input: Multispectral photoacoustic images (Mpi)
	Output: Estimation of cancer state (malignant or benign)
	Import the test images into the proposed model
	for each images Mpi = 1 to n do           // Ant colony optimization
	Calculate transition probability
	Update pheromone (P(Mpi))
	for each P(Mpi) images do
	Sk Construct the solution ()
	if fitness (Sk)<=fitness(Sbest) then
	Sbest Sk;
	end
	end
	While Stopping criterion not satisfied do // Particle swarm optimization
	for each image i = 1 to n do
	Sk = S(ni)
	if Sk > pbest then
	pbestn = Sk
	end
	for each Sk = 1 to n do
	Pgbest=(gbest |S(ni) = max(S(kn), k ϵ N(ni))
	Update velocity
	Update optimum result Finalbest
	end
	for each Finalbest = 1 to n do //RNN Classification
	Classifying the cancer state as malignant or benign
	end for
	end