An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images

Abstract

Early detection of Tuberculosis or TB can help in mitigating the chances of affecting the other body parts like the kidney, spine and brain, thereby reducing the death rate due to this disease. However, manual diagnosis by radiologists using Chest X-rays may include human error. Therefore, researchers have been trying hard to develop a computerized decision support system for efficient detection of TB from Chest X-ray images. In this work, we have proposed a model for screening TB using Chest X-ray images where the decisions from three base learners are combined using the type-1 Sugeno fuzzy integral based ensemble technique. Fuzzy measures required in this fuzzy integral based ensemble method are set experimentally in many state-of-the-art works. To overcome such manual tuning, we have used meta-heuristic optimization algorithms to set the fuzzy measures optimally during the training process of the model. The performance of the ensemble technique on the validation set is considered as the decider of the optimal fuzzy measures. Before applying the ensemble method we have extracted features from images using three state-of-the-art deep learning models, namely DenseNet121, VGG19 and ResNet50 pre-trained on imageNet dataset. With the above pre-trained models, the base learners are built using additional fully connected and softmax layers. We have evaluated the present work on a new and publicly available TB dataset consisting of Chest X-ray images. The obtained results (irrespective of the optimizer used) confirm that our method has outperformed state-of-the-art methods used for TB classification.