Tuberculosis X-Ray Images Classification based Dynamic Update Particle Swarm Optimization with CNN
The classification of tuberculosis (TB) based on chest X-Ray (CXR) remains a time-consuming activity that requires an expert’s interpretation. An automated TB classification on the CXR can be a significant clinical utility to overcome this issue as the disruptive technology is concerned. Most recent research focused on deep learning solutions but identifying the suitable network architecture remains a challenge as it depends on the image features. One of the network architectures is at the classification layer. This paper highlighted a proposed hybrid CNN and enhanced Particle Swarm Optimization (CNN-ePSO) to find an optimal architecture of a connected layer at the classification network layer. We proposed a discrete and real value representation of the particle and a dynamic update strategy of the particle. A series of experiments are performed using Montgomery and Shenzhen CXR for the image classification performance. Formulation of a suitable particle representation has shown a workable particle representation and successfully achieved its aim. The outcome assesses that the hybrid CNN-ePSO with image enhancement is superior to the CNN-PSO without image enhancement and other single CNN models with a remarkable improvement. Thus, a novel ePSO algorithm embedded with CNN captures significant attention on the classification result, mainly for CXR images. In the future, additional work on deep feature layer optimization would be possible for a better result and application of the most recent algorithm like cuckoo search and firefly algorithm.
Keywords: image classification, convolution neural network, deep learning, X-ray Images, particle swarm optimization.
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