This study is conducted to determine effectiveness and perspectives of application of the transfer learning approach to the medical images classification task. There are a lot of medical studies that involve image acquisition, such as XRay radiography, ultrasonic scanning, computer tomography (CT), magnetic resonance imaging (MRI) etc. Besides those medical procedures there are different operations that use medical images processing including but not limited to digital radiograph reconstruction (DRR), radiotherapy planning, brachy therapy planning. All those tasks could be effectively performed with help of software capable to perform segmentation, classification and object recognition. Those capabilities are naturally depend on neural classifiers. Presented work investigates different approaches to solving image classification task with neural networks, specifically, using pre-processing for feature extraction and end-to-end application of convolutional neural networks (CNN). Due to requirement of significantly big datasets and large computing power CNNs sometimes may appear difficult to train, so our results pay attention to application of transfer learning technique that can potentially relax requirements to classifier training. The conclusions of this study state that transfer learning can be effectively used for classification tasks, especially texture classification.
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