Performance Analysis of State-of-the-Art CNN versus a Capsule Network for Cell Image Classification
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Despite the state of the art performance on object recognition and image classification problems, CNNs are considered to have two significant weaknesses. Firstly, their inability to cater to changes in object orientation, position or lighting. Secondly, their inability to deal with part-whole relationships between objects. Capsule Networks are an enhancement to CNNs to more closely model the viewpoint invariance capability of human vision. The application of Capsule Networks to well-known datasets, such as MNIST and NORB, has achieved a state of the art performance, while the application to other datasets has had mixed results. The application of Capsule Networks to domains such as medical-based imaging problems is of significant interest as they have been shown to train accurately on some datasets with limited training data. Research undertaken by Liam Murphy (MSc AI) on the Performance of a State-of-the-Art CNN versus a Capsule Network for Cell Image Classification presented as part of the Irish Machine Vision & Image Processing Conference #IMVIP2020