Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging
CoRR(2024)
Abstract
Most efforts in Computer Vision focus on natural images or artwork, which
differ significantly both in size and contents from the kind of data biomedical
image processing deals with. Thus, Transfer Learning models often prove
themselves suboptimal for these tasks, even after manual finetuning. The
development of architectures from scratch is oftentimes unfeasible due to the
vastness of the hyperparameter space and a shortage of time, computational
resources and Deep Learning experts in most biomedical research laboratories.
An alternative to manually defining the models is the use of Neuroevolution,
which employs metaheuristic techniques to optimize Deep Learning architectures.
However, many algorithms proposed in the neuroevolutive literature are either
too unreliable or limited to a small, predefined region of the hyperparameter
space. To overcome these shortcomings, we propose the Chimera Algorithm, a
novel, hybrid neuroevolutive algorithm that integrates the Artificial Bee
Colony Algorithm with Evolutionary Computation tools to generate models from
scratch, as well as to refine a given previous architecture to better fit the
task at hand. The Chimera Algorithm has been validated with two datasets of
natural and medical images, producing models that surpassed the performance of
those coming from Transfer Learning.
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