## 3 Materials and Methods
### Databases and Image Pre-Processing
#### 3.1.2 DermIS Database  
The DermIS database stands as the most comprehensive repository of dermatological information available on the internet. It encompasses a wide array of images depicting nearly all categories of skin conditions and diagnoses, supplemented with differential diagnoses, case reports, and additional pertinent data such as medical journals. This collaborative project is conducted in conjunction with the Department of Socio-Clinical Medicine at the University of Heidelberg and the Department of Dermatology at the University of Erlangen. Within this database, there are 500 images portraying melanoma cases and another 500 images depicting healthy skin conditions [41; 42]. Much like our utilization of the ISIC database, this paper similarly leverages the entire collection of 1000 images within the DermIS database. Out of these images, 700 were allocated for training purposes, while the remaining 200 were dedicated to testing the proposed algorithms. Examples of images drawn from the DermIS Database are showcased in Figures 4 and 5. As commonly understood, the development of a highly effective medical diagnosis system for image classification necessitates the utilization of high-quality input images. Given the absence of suitable databases, image pre-processing becomes an imperative step. Enhancing image quality is pivotal, as it facilitates subsequent processing stages. Initially, this entails adjustments to brightness and contrast, often followed by converting color images to grayscale. The selection of specific image processing techniques hinges on the nature of the classification task at hand. These techniques encompass noise reduction, image resizing, color correction, segmentation, and feature extraction [43; 44; 45]. In this study, we employ pre-processing techniques that involve adjusting image dimensions through resizing and eliminating hair artifacts. The resizing is tailored to the specific requirements of each CNN utilized. For instance, in the case of the GoogLeNet architecture, images were resized to dimensions of 256 \(\times\) 256 \(\times\) 3 pixels, and the aspect ratio was 1:1 (this is the case with all the images we pre-processed). Concerning skin lesion images, certain elements can be regarded as image artifacts, as they impede the computer-aided diagnosis process. Notably, some images suffer from blurriness due to either human movements (by the operator or patient) or the presence of hair or water droplets. Additionally, annotations, such as measuring scales, might be present in certain images, potentially misleading the system. Our research is dedicated to propelling the field of dermatology and the precise classification and diagnosis of skin conditions through meticulous analysis of skin lesions. In pursuit of this goal, we have deliberately chosen to incorporate the ISIC 2020 and DermIS datasets into our study. The selection of the ISIC 2020 and DermIS datasets for our research is substantiated by a range of factors. Firstly, these datasets hold considerable prominence and are widely employed in the realm of dermatology and the analysis of skin images. They have undergone exhaustive curation and validation by domain experts, instilling confidence in the quality and dependability of the data. Furthermore, the ISIC 2020 dataset centers specifically on images of skin lesions, presenting an extensive compilation of diverse cases encompassing melanoma, nevi, and various other prevalent skin conditions. This dataset showcases a diverse array of images, facilitating the training and assessment of our models across a broad spectrum of skin aliments, thereby amplifying the applicability of our findings. In tandem, the DermIS dataset supplements the ISIC 2020 dataset by furnishing an extensive trove of dermatological images, spanning both clinical and dermoscopic views. This dataset bestows invaluable insights into the visual attributes and patterns of diverse skin disorders, allowing us to delve into and dissect multifaceted aspects of dermatological ailments.