Transfer Learning Tuning Utilizing Grey Wolf Optimizer for Identification of Brain Hemorrhage from Head CT Images


Most commonly, diagnosing the brain hemorrhage - a condition caused by a brain artery busting and causing bleeding is done by medical experts identifying such pathologies from the computer tomography (CT) images. With great advancements in the domain of deep learning, utilizing deep convolutional neural networks (CNN) for such tasks has already proven to achieve encouraging results. One of the major problems of using such an approach is the need for big labeled datasets to train such deep architectures. One of the efficient techniques for training CNNs with smaller datasets is transfer learning. For the efficient use of transfer learning, many parameters are needed to be set, which are having a great impact on the classification performance of the CNN. Most of those parameters are commonly set based on our previous experience or by trial and error. The proposed method addresses the problem of tuning the transfer learning technique utilizing the nature-inspired, population-based metaheuristic Grey Wolf Optimizer (GWO). The proposed method was tested on a small head CT medical imaging dataset. The results obtained from the conducted experiments show that the proposed method outperforms the conventional approach of parameter settings for transfer learning.

StuCoSReC: proceedings of the 2019 6th Student Computer Science Research Conference