Improved Swarm Intelligence Optimization using Crossover and Mutation for Medical Classification

Yasen, Mais; Al-Madi, Nailah

doi:10.1109/ictcs.2019.8923027

Cited by 1 publication

(1 citation statement)

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“…This behavior of swarm-based techniques has been taken to advantage in hyperparameter selection which is a computationally expensive process as mentioned before. Bee colony optimization and dragonfly algorithm were used by Yasen and Al-Madi [ 26 ] to select the size of hidden nodes in the neural network. Crossover and mutation were also combined to devise new models.…”

Section: Related Workmentioning

confidence: 99%

Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence Models

Bhardwaj

Gupta

Guhan

et al. 2022

Computational and Mathematical Methods in Medicine

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Early diagnosis of retinal diseases such as diabetic retinopathy has had the attention of many researchers. Deep learning through the introduction of convolutional neural networks has become a prominent solution for image-related tasks such as classification and segmentation. Most tasks in image classification are handled by deep CNNs pretrained and evaluated on imagenet dataset. However, these models do not always translate to the best result on other datasets. Devising a neural network manually from scratch based on heuristics may not lead to an optimal model as there are numerous hyperparameters in play. In this paper, we use two nature-inspired swarm algorithms: particle swarm optimization (PSO) and ant colony optimization (ACO) to obtain TDCN models to perform classification of fundus images into severity classes. The power of swarm algorithms is used to search for various combinations of convolutional, pooling, and normalization layers to provide the best model for the task. It is observed that TDCN-PSO outperforms imagenet models and existing literature, while TDCN-ACO achieves faster architecture search. The best TDCN model achieves an accuracy of 90.3%, AUC ROC of 0.956, and a Cohen’s kappa score of 0.967. The results were compared with the previous studies to show that the proposed TDCN models exhibit superior performance.

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Section: Related Workmentioning

confidence: 99%