# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 1 Introduction  
Technological progress plays an essential role in the prevention, diagnosis, and treatment of illnesses and diseases and revolutionized healthcare, including patient diagnostic data and patient biological signal facilities and processing [1; 2; 3; 4]. Further breakthroughs are expected in various medical areas, particularly in oncology, since cancer is one of the most prevalent causes of death worldwide [5; 6]. Technology is projected to play a greater role in medical practice, and the application of computer-aided equipment is especially promising [7; 8; 9]. In particular, Computer-Aided Detection (CAD) systems are becoming an important tool in supporting physicians in cancer detection and prevention, particularly when diagnostic images are complex to analyze (due to low quality) and in a high number. The risk of physicians missing subtle abnormalities exists in these situations, especially because images are often low in contrast. In cases of dubious diagnosis, it is common practice for clinicians to behave cautiously and, in order to avoid missing the identification of a malignant case, to prescribe invasive diagnostic procedures, such as biopsies. Inevitably, such a prudent approach results in a high number of negative biopsies with high economic costs. Melanoma is the most aggressive and deadly form of skin cancer, accounting for about \(75\%\) of deaths caused by skin cancer. Its incidence has grown by \(15\%\) over the last 10 years compared with the previous decade [10; 11]. Early diagnosis is critical and results in a better prognosis. The five-year survival is approximately \(98.4\%\) for patients with early stage melanoma compared to just \(22.5\%\) for patients with metastatic melanoma [12; 13]. Different algorithms have been proposed in the literature for a more objective diagnosisof skin lesions. They rely on identifying various features, and they classify the lesion's nature according to feature occurrence or non-occurrence [14; 15; 16]. Some of them aid decision making by calculating a score (such as the ABCD/ABCD rule and the seven-point checklist), while others adopt qualitative criteria (such as the Menzies method) [17]. For example, the ABCD algorithm considers the asymmetry of the lesion area, border irregularity, color variegation and diameter, or differential structures of the area under examination. A scoring system has been developed to calculate the total dermoscopy score (TDS) using a linear equation. The TDS allows the grading of lesions and an estimate of their probability of being malignant. A low score means that the lesion is benign (TDS < 4.75), a middle score can be interpreted as suspicious (4.75 < TDS < 5.45), and a high score means that the lesion is likely to be a malignant melanoma (TDS > 5.45)  
Several non-invasive imaging techniques (such as dermoscopy, confocal scanning laser microscopy, optical coherence tomography, ultrasound, and magnetic resonance) have also been used to support physicians and allow an early diagnosis of malignant skin lesions [18]. These techniques magnify and enhance skin lesions, but early diagnosis by imaging remains quite challenging [19] because there are many different inflammatory skin/lesions and conditions, each with a complex and variable appearance. As the morphology of a skin lesion directly influences the diagnosis, slight morphological differences related to different diseases are difficult to notice without considerable expertise in the specific field. Secondly, simple visual information can lead to misdiagnosis because different pathologies can display similar lesions and because the same lesion can have a different appearance depending on the individual, body part, and stage. Lastly, any diagnosis involving human factors can be difficult to reproduce because of high intra- and inter-observer variability [20; 21]. These considerations explain the keen interest in designing CAD systems able to classify pigmented skin lesions and monitor their evolution.