# MR Images, Brain Lesions, and Deep Learning
## 5 Discussion and Conclusions  
The techniques of deep learning are going to play a major role in medical diagnosis in the future, and even with the high training cost, CNNs appear to have great potential and can serve as a preliminary step in the design and implementation of a CAD system [40]. However, brain lesions, especially WMHs, have significant variations with respect to size, shape, intensity, and location, which makes their automatic and accurate segmentation challenging [197]. For example, even though stroke is considered to be easy to recognize and differentiate from other WMHs for experienced neuroradiologists, it could be a challenge and a difficult task for general physicians, especially in rural areas or in developing countries where there are shortages of radiologists and neurologists, and for that reason, it is important to employ computer-assisted methods as well as telemedicine [213,214]. [PERSON] and [PERSON] [215] state that the current COVID-19 pandemic situation further underscores the importance of telemedicine in neurology and other health aspects (e.g., ophthalmology [216]), is no longer a futuristic concept, and has become new normal (see, for example, [217]). An example of the utility of telemedicine is the success experience reported by [PERSON] et al. [218], who detail how telemedicine during the COVID-19 pandemic is providing rapid access to specialists who are unavailable in West China, a region that does not have many economic resources or healthcare infrastructure when compared to the eastern part of the country [218]. It should be noted that telemedicine \"was more a concept than a fully developed reality\" [215], due principally to limitations such as a lack of financial resources, technological infrastructure, regulatory protocols, safety data, trained people, ethical questions, etc. [215,218,219]; these aspects are especially challenging in developing countries [220,221]. To identify stroke, according to [PERSON] et al. [72], the SVM method provides better prediction and quantitative metrics compared to the ANN. In addition, they note that the SVM provides accurate prediction with a small sample size [72,222]. [PERSON] et al. [124]indicate that the biggest barriers in applying deep learning techniques to medical data are the insufficiency of the large datasets that are needed to train deep neural networks (DNNs) [124]. In the ISLES 2015 [26] and ISLES 2016 [75] competitions, the best results were obtained for stroke lesion segmentation and outcome prediction using the classic machine learning models, specifically the random forest (RF), whereas in ISLES 2017 [75], the participants offered algorithms that use CNNs, but the overall performance was not much different from ISLES 2016. However, the ISLES team states that despite this, deep learning has the potential to influence clinical decision making for stroke lesion patients [75]. However, this is only in the research setting and has not been applied to a real clinical environment, in spite of the development of many CAD systems [116]. Although various models trained with small datasets report good results (DSC values > 0.90) in their classifications or segmentations (Table 4 [21, 77, 190]), [PERSON] [223] recommends avoidance of methods trained with small datasets because of replicability and reproducibility issues [90, 223]. Therefore, it is important to have multidisciplinary groups [90, 111, 224] involving representatives from the clinical, academic, and industrial communities in order to create efficient processes that can validate the algorithms and hence approve or refute recommendations made by software [90]. Related to this is that algorithmic development has to take into consideration that real-life performance by clinicians is different from models. However, other areas of medicine, for example, ophthalmology, have shown that certain classifiers approach clinician-level performance.