# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 1 Introduction  
Over the last two decades, cases of diabetes mellitus (DM) have increased noticeably across global public health systems [1, 2]. In 1985, 2000, and 2010, there were 30, 177, and 185 million cases respectively [3, 4]. Epidemiological studies suggest that the estimated number of patients with DM will be greater than 360 million by 2030. Patients with DM can acquire numerous complications suchas DFU (diabetic foot ulcer) and this particular issue has shown an increasing trend in the past few decades [5; 6; 7]. In general, the percentage of diabetic patients who suffer from DFU over their lifespan is 15% [8]. Obtaining accurate figures related to the occurrence of DFU is a challenge, but it can be in the range of 4-27% [9; 10; 11]. Currently, DFU is counted as a primary source of hospitalization and is the most important cause of morbidity in diabetic patients [1; 5; 12; 13]. The expectancy of DFU among DM patients is around 20% of hospital admittances [14]. More specifically, once a patient acquires DFU, there is an increased risk of ulcer evolution [14], which could finally lead to amputation. According to earlier studies, the cost of a single ulcer treatment was around $17,500. In Europe and North America, between 7 and 20% of the total expenditure on diabetes could potentially be attributed to DFU if all costs are considered. The process of evaluating DFU consists of different imperative tasks in premature diagnosis, tracking progress, and several long-lasting activities during DFU management and treatment based on each case. This evaluation process includes: a) evaluating the patient's medical history, b) examination of the DFU by a diabetic foot specialist and c) supplementary tests such as X-Ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans could be valuable for developing the treatment plan. Thus, computer vision (CV) algorithms are needed to evaluate visual appearances like textures, features and color descriptors. Using automatic telemedicine systems for DFU recognition is still in its early stages. In 2015, [PERSON] et al. [15; 16] employed 3D surface reconstruction, infrared thermal images, and spectral imaging to develop a smart telemedicine system. Implementing such a system requires several cost-effective devices as well as expert training in using these devices. For classification, an image-capture box was utilized by [PERSON] et al. [17] to determine the DFU area and to pick up image data. It was based on a cascaded two-stage support vector machine (SVM). They presented a super-pixel method for segmentation and they extracted several features to execute the two-stage classification. Note that this system was not applied to a big dataset, although it reported favorable results. Furthermore, to capture the images, a touching-base between the box surface and the patient's feet was required. However, according to the health setting, this was disallowed due to worries about controlling the infection. Thus, the image-capture box was unworkable for image-data gathering. [PERSON] et al. [18] have performed the DFU segmentation task. They have also surrounded skin on the full foot images. Computer techniques constructed via image processing methods or manually engineered features were developed to segment and classify various tissues in correlated skin lesions. For classification, there are two stages for traditional machine learning. The first stage is to extract different features such as color and texture descriptors on undersized outlined patches related to wound images. The second stage is the classification of these patches into either normal or abnormal skin [19; 20; 21; 22]. However, skin color is based on the patient's ethnicity group, while handcrafted features are influenced via the lighting conditions in most CV systems. Overall, every skin lesion that belongs to an ulcer or wound is currently named as the wound.