Detecting maleficent medical conditions
, especially cancers, is a costly and complicated task. Mostly only a biopsy can determine whether a tumour is cancerous, which causes the patient inconvenience and risk of the operation, as well as extra cost in the medical system. In case of many tumours a non-invasively taken image can also provide information on the nature of the formula, or at least a guidance whether to take biopsy, remove the tumour immediately, or leave it, because the removal is an unnecessary risk in case of a non-maleficent formula.
The medical staff is trained to identify maleficent tumours of their specialization field, but an automatic image processing can help their decision, moreover in some cases, like the skin cancers, it could add a tool into the hands of the not specialized persons as well. The non-invasive medical imaging  contains both the visible light techniques (like endoscopes and microscopes) as well as the higher and lower frequency electromagnetic field based imaging (like X-ray and ultrasound reflection based images). In case of tumours many tell-tale signs can be from the variation of the blood vessel structure around the formula [4-6] to the pattern and colour of the surface of the polyp itself [7-10].
Five INNOSOC students, supervised by two INNOSOC lecturers, will collaborate on answering how ICT can be used in image-based cancer detection. These activities will be conducted as a part of the ERASMUS+ blended mobility and will be finalized during INNOSOC Zagreb 2016 workshop in late April 2016.
Medical image processing is one of positive side effects of the fast proliferation of the ICT into all domains of the society
. For example, it can decrease the workload put on medical staff and help them decide in problematic questions or draw attention to smaller details where problems might be present.
Cancer detection is still made by humans, and it will remain so, however, a visual aid can increase effectiveness, and a pre-screening can still be carried out by less qualified personnel and intelligent computer programmes instead of fully trained medical specialists.
The aim of this Case Study is to summarize the technologies used in image-based cancer detection for some types of cancer and compare the applicability of the techniques to the various cases. It is also necessary to determine whether new image processing methods could be used. In most cases the decision about a formula is taken in a crisp, yes or no way, however, many soft decision techniques can also be applied, completed with a learning algorithm, thus it is also necessary to map the applied training algorithms and their efficiency.
Therefore, this Case Study specifically addresses the “Health, demographic change and wellbeing” H2020 challenge.
This Case Study is tightly connected with innovation, intercultural and ICT.
First, the innovation aspect emerges from comparing multiple types of image processing methods used for various purposes that can lead to a common method or a method applied for one type of problem to be applicable in other problems.
Second, although medical image processing is an international problem, acquiring images has different aspects in different cultures. Additionally, interpreting and communicating the results has various cultural aspects as well.
Third, medical images are processed by IT devices, and their transmission has multiple ICT tasks from coding, compressing to videoconferences about the results.
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