# Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
## 4 Experiments
### Applications and Comparison  
Convolutional neural network for Human Epithelial-2 (HEp-2) cell image classification, fetal hypoxia detection based on transfer learning approach is a viable solution to serious medical needs [86, 87]. Transfer learning for pediatric pneumonia diagnosis, lung pattern analysis, and computer-aided diagnosis of breast ultrasound images using transfer learning are some examples where introducing technology helps doctors speed up the treatment process. It also, enhances the early detection of the symptoms of the medical condition and can help a patient in critical need by making it possible to quickly evaluate the case [88, 89, 18]. Applying transfer learning in biomedical image analysis is a very promising domain and supports general purpose cause [89].  
Figure 4: The learning curves of STL and HTL for homogeneous and heterogeneous datasets. Data-plot, (a,b) show experiment results discussed in Table 6, using dataset from Table 4.  
The ratio of patients and available specialists in medical profession is very high, especially in case of some very critically sensitive conditions, where the professionals are over-burdened. However, with studies such as froth image analysis, by use of transfer learning and convolutional neural networks, transfer learning for diabetic retinopathy fundus image classification the treatment can be easily available to larger percentage of patients in less time [90,91]. Transfer learning method for pediatric pneumonia diagnosis [18] is a very widely applied research, as kids are easy targets for diseases like pneumonia. Transfer learning for molecular cancer classification [20] and digital mammographic tumor classification [21], where the study of computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions, can be an answer to early detection of a terminal disease.  
Transfer learning using X-ray baggage security imagery where, within the context of X-ray security screening, limited availability of training for particular items of interest can, thus, pose a problem. To overcome this issue, a transfer learning paradigm, such as a pre-trained CNN, primarily trained for generalized image classification tasks where sufficient training data exists, can be specifically optimized as a later secondary process that targets (specific) this application domain [22]. Similarly, transfer learning for diabetic retinopathy fundus image classification, artificial intelligence in fracture detection [91,92], and sepsis classification [93] are a few of the progressive research domains for transfer learning. Some other interesting applied works are the millet crop images study [94], online fault diagnosis [95], and decision support from financial disclosures [96].  
Transfer learning is highly applicable in other domains, such as Natural language processing (NLPs) with automatic speech recognition systems [97]. Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. In specific cases, unsupervised transfer learning can be useful for biomedical applications [98].  
Another futuristic application is using transfer learning and transferring the knowledge (connection weight parameters) from learned neural networks, such as CNNs to hardware vision devices, such as using FPGAs, so that the learned knowledge can be used in small and mobile objects, such as robots, cars, and other mobile vehicles, for example. As discussed, proposed approach can be further extended to target applications, such as image segmentation, object recognition for robotic manipulation, or pedestrian or obstacle detection for autonomous vehicles.